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Paper Citation Record · LEDGER

How to implement the Bayes' formula in the age of ML?

As of 18 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 1 inbound Pith citation observation for arXiv:2411.09653.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.09653 v1

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:33:11.655782Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T18:41:31.982850Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T18:46:29.553182Z

Reference resolution

73 of 73 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4fd4bb5-47c4-4ded-9dae-5d4ae153681b · outbound

This paper cites write newline.

How to implement the Bayes' formula in the age of ML? write newline

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 6e16a9d3-b90c-4f16-922d-48f717bf0a83 · outbound

This paper cites an unresolved cited work.

How to implement the Bayes' formula in the age of ML? Unresolved cited work

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ea7881a2-2f2c-468f-81f8-3277af7d3182 · outbound

This paper cites Data-Driven Approximation of Stationary Nonlinear Filters with Optimal Transport Maps.

How to implement the Bayes' formula in the age of ML? Data-Driven Approximation of Stationary Nonlinear Filters with Optimal Transport Maps

Reference 3

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verified exact
local_arxiv, observed 2026-08-12T20:33:12.405804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a79b7308-d970-41a4-a651-77eca82b110e · outbound

This paper cites title Nonlinear filtering with B renier optimal transport maps.

How to implement the Bayes' formula in the age of ML? title Nonlinear filtering with B renier optimal transport maps

Reference 4

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verified exact
arxiv_id, observed 2026-08-12T20:33:12.382401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a94e5b43-fdd3-490a-a412-68206b988764 · outbound

This paper cites Input Convex Neural Networks.

How to implement the Bayes' formula in the age of ML? Input Convex Neural Networks

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:33:11.282570Z digest=sha256:04e5343c26ff86992bca557f0c45322092178d32e18b40b95449bbac9ad8e1c8

Observation b075a428-93ac-4cc9-a94d-92ac4ee17749 · outbound

This paper cites title A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking.

How to implement the Bayes' formula in the age of ML? title A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking

Reference 6

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 0e9868cf-113d-4d3c-b59c-eb30ecde51a2 · outbound

This paper cites title Estimation with applications to tracking and navigation: theory algorithms and software , publisher John Wiley & Sons.

How to implement the Bayes' formula in the age of ML? title Estimation with applications to tracking and navigation: theory algorithms and software , publisher John Wiley & Sons

Reference 7

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 75db0b45-8526-4c4b-91d3-945ab6ba32a4 · outbound

This paper cites Freedman , volume 2 , publisher Institute of Mathematical Sciences , pages 316--334.

How to implement the Bayes' formula in the age of ML? Freedman , volume 2 , publisher Institute of Mathematical Sciences , pages 316--334

Reference 8

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation f00fac05-c211-44d0-aa64-7c28d618ca46 · outbound

This paper cites title An ensemble kalman-bucy filter for continuous data assimilation.

How to implement the Bayes' formula in the age of ML? title An ensemble kalman-bucy filter for continuous data assimilation

Reference 9

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8e7e6708-cda8-484c-a30a-713c2c33c128 · outbound

This paper cites title Error bounds and normalising constants for sequential M onte C arlo samplers in high dimensions.

How to implement the Bayes' formula in the age of ML? title Error bounds and normalising constants for sequential M onte C arlo samplers in high dimensions

Reference 10

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b91ff844-2cbf-4e51-b02d-ed1060abc2d2 · outbound

This paper cites Ghosh , publisher Institute of Mathematical Statistics , pages 318--329.

How to implement the Bayes' formula in the age of ML? Ghosh , publisher Institute of Mathematical Statistics , pages 318--329

Reference 11

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 87b6498f-c7c4-4ca4-884b-b5841c39e2ac · outbound

This paper cites title Applied optimal control: Optimization.

How to implement the Bayes' formula in the age of ML? title Applied optimal control: Optimization

Reference 12

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8fa5c45f-d1c1-457c-8a23-06a112ffffa5 · outbound

This paper cites title A survey of numerical methods for nonlinear filtering problems.

How to implement the Bayes' formula in the age of ML? title A survey of numerical methods for nonlinear filtering problems

Reference 13

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 82763b5c-f06a-4d77-92b8-b7058e5ea647 · outbound

This paper cites title Supervised training of conditional M onge maps.

How to implement the Bayes' formula in the age of ML? title Supervised training of conditional M onge maps

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 51a6a0d9-e9a7-4064-928d-173f95b50013 · outbound

This paper cites title An overview of existing methods and recent advances in sequential monte carlo.

How to implement the Bayes' formula in the age of ML? title An overview of existing methods and recent advances in sequential monte carlo

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1073eac3-c377-4eb3-a096-1dea352d035f · outbound

This paper cites an unresolved cited work.

How to implement the Bayes' formula in the age of ML? Unresolved cited work

Reference 16

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation d353825e-c881-48a3-b70c-69a48bb2e868 · outbound

This paper cites title Vector quantile regression: an optimal transport approach.

How to implement the Bayes' formula in the age of ML? title Vector quantile regression: an optimal transport approach

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation f51f09b8-2fc6-4b04-abc5-361fa86a91fd · outbound

This paper cites A McKean optimal transportation perspective on Feynman-Kac formulae with application to data assimilation.

How to implement the Bayes' formula in the age of ML? A McKean optimal transportation perspective on Feynman-Kac formulae with application to data assimilation

Reference 18

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7f3c1a7e-c77a-4dec-9a70-4cc6e10daddf · outbound

This paper cites title Intrinsic methods in filter stability.

How to implement the Bayes' formula in the age of ML? title Intrinsic methods in filter stability

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a35ab7b7-e756-44ff-975d-1c7e7d516a3a · outbound

This paper cites title The Oxford handbook of nonlinear filtering , publisher Oxford University Press.

How to implement the Bayes' formula in the age of ML? title The Oxford handbook of nonlinear filtering , publisher Oxford University Press

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9cf3ca6b-1d39-4769-a4ce-c090ce211d0f · outbound

This paper cites title Approximate M c K ean- V lasov representations for a class of SPDE s.

How to implement the Bayes' formula in the age of ML? title Approximate M c K ean- V lasov representations for a class of SPDE s

Reference 21

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 5a3672d6-08fb-40c3-ab91-ead114491bc2 · outbound

This paper cites Inproceedings.

How to implement the Bayes' formula in the age of ML? Inproceedings

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 3ffec719-ce15-4245-80d7-67cd053051da · outbound

This paper cites Inproceedings.

How to implement the Bayes' formula in the age of ML? Inproceedings

Reference 23

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Source-reported events for the cited work

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Observation eb7dce77-e6d0-4c94-bb73-eb970690738e · outbound

This paper cites an unresolved cited work.

How to implement the Bayes' formula in the age of ML? Unresolved cited work

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 99863920-1e2b-4ae9-9b2c-b17299d6de2b · outbound

This paper cites Stochastic Particle Flow for Nonlinear High-Dimensional Filtering Problems.

How to implement the Bayes' formula in the age of ML? Stochastic Particle Flow for Nonlinear High-Dimensional Filtering Problems

Reference 25

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b7ffc749-49fb-41c5-8a4f-08ec0644930a · outbound

This paper cites Inproceedings.

How to implement the Bayes' formula in the age of ML? Inproceedings

Reference 26

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 94c8dd60-a3cb-43bb-bca3-b7724f052460 · outbound

This paper cites an unresolved cited work.

How to implement the Bayes' formula in the age of ML? Unresolved cited work

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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This paper cites title A tutorial on particle filtering and smoothing: F ifteen years later.

How to implement the Bayes' formula in the age of ML? title A tutorial on particle filtering and smoothing: F ifteen years later

Reference 28

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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How to implement the Bayes' formula in the age of ML? ( year 2001 )

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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This paper cites title Bayesian inference with optimal maps.

How to implement the Bayes' formula in the age of ML? title Bayesian inference with optimal maps

Reference 30

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation dd8605ce-0a95-4535-bb75-d61993c1b582 · outbound

This paper cites title Sequential data assimilation with a nonlinear quasi-geostrophic model using M onte C arlo methods to forecast error statistics.

How to implement the Bayes' formula in the age of ML? title Sequential data assimilation with a nonlinear quasi-geostrophic model using M onte C arlo methods to forecast error statistics

Reference 31

Resolution
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Source-reported events for the cited work

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How to implement the Bayes' formula in the age of ML? title Data Assimilation

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7f33e50e-a311-497f-ad50-d832f2f06b71 · outbound

This paper cites an unresolved cited work.

How to implement the Bayes' formula in the age of ML? Unresolved cited work

Reference 33

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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How to implement the Bayes' formula in the age of ML? title Computational optimal transport and filtering on riemannian manifolds

Reference 34

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 782be83c-d75c-445b-8943-2afb8cc30c6c · outbound

This paper cites title Monte carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering.

How to implement the Bayes' formula in the age of ML? title Monte carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.849880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.439080Z digest=sha256:d408f63065ccc2ac69df7c027bc4bb1ac0c21f83e9985a721e9ad112e037c603

Observation 3dd74f06-713a-4bce-a3bd-6cc0f55819cf · outbound

This paper cites Gibbs flow for approximate transport with applications to Bayesian computation.

How to implement the Bayes' formula in the age of ML? Gibbs flow for approximate transport with applications to Bayesian computation

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-08-12T20:33:11.925177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.444564Z digest=sha256:726a9ebb42411def569a760c670b1d5737b7d06188a8b343f2579f4718bfd4ab

Observation 22af288c-29ec-47c1-a5c1-44422af7ea27 · outbound

This paper cites title Denoising diffusion probabilistic models.

How to implement the Bayes' formula in the age of ML? title Denoising diffusion probabilistic models

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.834183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.449555Z digest=sha256:da146777eab1e11394d9d6551882db69b242f825112496ba73b86de0b46cb750

Observation ab60c79f-462f-4145-845e-44eaecfa0218 · outbound

This paper cites title A sequential ensemble K alman filter for atmospheric data assimilation.

How to implement the Bayes' formula in the age of ML? title A sequential ensemble K alman filter for atmospheric data assimilation

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.818920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.454242Z digest=sha256:fb7707be71e8673b5e6c8b0efebf792d46e9a2e7dc417af8cfe85ca61a1040a9

Observation e3184098-b65c-4b11-95f6-def69cb7b4e8 · outbound

This paper cites title Stochastic processes and filtering theory , publisher Courier Corporation.

How to implement the Bayes' formula in the age of ML? title Stochastic processes and filtering theory , publisher Courier Corporation

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.804502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.458770Z digest=sha256:9ae8201fe9a198bda324ac5c5dc0abd8456226f2da6fc5884564c43542924a27

Observation 3a907e4b-56a7-4613-99cf-998efdae0958 · outbound

This paper cites title Linear Estimation , publisher Prentice Hall.

How to implement the Bayes' formula in the age of ML? title Linear Estimation , publisher Prentice Hall

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.789341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.463126Z digest=sha256:8be7f8dc405d27318e0cdaff6d06d54bc4f1abed64ee4d8ab59936f625ba33be

Observation 4debbcaf-a0e6-4470-b40c-69a6268275e0 · outbound

This paper cites title A new approach to linear filtering and prediction problems.

How to implement the Bayes' formula in the age of ML? title A new approach to linear filtering and prediction problems

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.774428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.467495Z digest=sha256:34bcce11f5e0bece5c50802254128bdbd1a0f69c65b2e0cb54d4fce79a01a7a2

Observation 60956e41-6aa6-4c06-b387-1768fbada626 · outbound

This paper cites title New results in linear filtering and prediction theory.

How to implement the Bayes' formula in the age of ML? title New results in linear filtering and prediction theory

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-12T20:33:11.476123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:33:11.476123Z digest=sha256:f36932693c79afa62bc0ae5e39cd53aaed063ee65a9791db18ac7780693b2517

Observation da3a0e94-e712-47c0-ac04-294768bc1388 · outbound

This paper cites title Duality for nonlinear filtering i: Observability.

How to implement the Bayes' formula in the age of ML? title Duality for nonlinear filtering i: Observability

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.759223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.486205Z digest=sha256:73739740d9358c6cb4af4c286f61228d48f2777dccd6e07e5763f0f9f579d480

Observation e38ef278-bd6a-4c18-96e7-800a124588a7 · outbound

This paper cites Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference.

How to implement the Bayes' formula in the age of ML? Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-12T20:33:11.491802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:33:11.491802Z digest=sha256:aac728f49c3510225688b4daed22f117d96ac3750ea9d86f72d67d39f9b2bd2c

Observation 8311c49b-528f-4f03-baa1-f9fb1c262afd · outbound

This paper cites an unresolved cited work.

How to implement the Bayes' formula in the age of ML? Unresolved cited work

Reference 45

Resolution
unresolved
raw_fallback, observed 2026-08-12T20:33:12.744198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.500075Z digest=sha256:2193719e562cf77a2da45e27c0290c12f45e26de4962704738984cf16ef83729

Observation 85f04d99-ee3c-44bb-9400-6efa8f7203f2 · outbound

This paper cites An introduction to sampling via measure transport.

How to implement the Bayes' formula in the age of ML? An introduction to sampling via measure transport

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-12T20:33:11.505609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:33:11.505609Z digest=sha256:88aa694820ec1c7a26a2ac8d4c028623070da342a082ded45fd351b2dfcdcabf

Observation c606cffa-99c3-47f7-a5af-a58fd5d15b90 · outbound

This paper cites Coleman T ( year 2019 ).

How to implement the Bayes' formula in the age of ML? Coleman T ( year 2019 )

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.729046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.511258Z digest=sha256:587a807f0b71c8e98d3424ec3d602946ef4ef903c8c11c40b39804158386538a

Observation c50e42e0-051b-4f9b-a0c6-fce46057605d · outbound

This paper cites title Markov chains and stochastic stability , publisher Springer Science & Business Media.

How to implement the Bayes' formula in the age of ML? title Markov chains and stochastic stability , publisher Springer Science & Business Media

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.714183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.516491Z digest=sha256:1ba24b3b2e73403db4deec74e8216a1b4957c5fc38f3d021de3ad33d96576c13

Observation 58f83196-f8e6-4b7b-8879-e17175d8ecbe · outbound

This paper cites title Asymptotic stability of the optimal filter with respect to its initial condition.

How to implement the Bayes' formula in the age of ML? title Asymptotic stability of the optimal filter with respect to its initial condition

Reference 49

Resolution
verified exact
doi, observed 2026-08-12T20:33:11.780439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.521938Z digest=sha256:d1d5e5874e1f06557fa6a0589d65a2cdd5bd8599c1386b16cf537830c7d051c9

Observation 74de2c20-e7b5-4843-99ed-2524ce65d43b · outbound

This paper cites ( year 2015 ).

How to implement the Bayes' formula in the age of ML? ( year 2015 )

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.698452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.527244Z digest=sha256:0d0cedb23fc6bf1ad504ab6ee6c74f9838f6cc68556930c588a462dca52cf49c

Observation 73fe9992-a7d9-4ee7-bf30-b7c5fc77c58d · outbound

This paper cites title A dynamical systems framework for intermittent data assimilation.

How to implement the Bayes' formula in the age of ML? title A dynamical systems framework for intermittent data assimilation

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-12T20:33:11.532619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:33:11.532619Z digest=sha256:041d1143434bcb519e7ec221b8a21e78aa1144d2a2b4f6d898f6ce6b4634fdbd

Observation f344cd74-2236-4e64-ab13-cf9dfdc5d01d · outbound

This paper cites title A nonparametric ensemble transform method for B ayesian inference.

How to implement the Bayes' formula in the age of ML? title A nonparametric ensemble transform method for B ayesian inference

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.683035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.539265Z digest=sha256:8141049ed17525fe7184df227ecac98a1664bb748b37d069ff87ae9a23d22744

Observation cc76ad54-5310-489d-a42f-091e25a09630 · outbound

This paper cites title Data assimilation: The S chr \"o dinger perspective.

How to implement the Bayes' formula in the age of ML? title Data assimilation: The S chr \"o dinger perspective

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.668368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.544369Z digest=sha256:b6b00f5a3f154ac8a1ce7c95a0615cbc95ea82e7e545b6c922e0a77474f073cf

Observation cc9732e6-6a36-427c-a4e2-dd75b2da5f7e · outbound

This paper cites title Probabilistic forecasting and Bayesian data assimilation , publisher Cambridge University Press.

How to implement the Bayes' formula in the age of ML? title Probabilistic forecasting and Bayesian data assimilation , publisher Cambridge University Press

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.653603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.549791Z digest=sha256:b8a271e98779eb2ffa7ce3817501684cf66f562b47712a57d22cfdbeb8915d2a

Observation 8598ad4f-5ba1-4252-83e4-3e92ecacd627 · outbound

This paper cites title Beyond the K alman filter.

How to implement the Bayes' formula in the age of ML? title Beyond the K alman filter

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.638476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.554933Z digest=sha256:e492dd1e8e68e64aa6864cd84c798535203dc168bc1deca71255b2a652d22a5b

Observation 0f5ebe06-2530-4c51-9549-ea5137f96642 · outbound

This paper cites an unresolved cited work.

How to implement the Bayes' formula in the age of ML? Unresolved cited work

Reference 56

Resolution
unresolved
raw_fallback, observed 2026-08-12T20:33:12.623525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.560141Z digest=sha256:31e8658f0a0d349f6a97a4fd39bff987a6518f7ada20316f21f57ea714d73aa9

Observation 36a91a1d-d9e3-4128-94ba-5ca2ae41184b · outbound

This paper cites Preconditioned training of normalizing flows for variational inference in inverse problems.

How to implement the Bayes' formula in the age of ML? Preconditioned training of normalizing flows for variational inference in inverse problems

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-12T20:33:11.565792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:33:11.565792Z digest=sha256:5275fcdb70c33c4a3b105a35cd3dae14fef088a0ee98630ce0726d06a7576cbf

Observation 5122b761-2f86-45d7-805d-250a4857a3b1 · outbound

This paper cites title Coupling techniques for nonlinear ensemble filtering.

How to implement the Bayes' formula in the age of ML? title Coupling techniques for nonlinear ensemble filtering

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.607997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.571433Z digest=sha256:1fff9881741c67fca5634e78b7b61585d97a59c6b42638e857636ca9ba922f9c

Observation a5ccab6d-2b1d-487f-88a0-a875bdb89464 · outbound

This paper cites title How to avoid the curse of dimensionality: Scalability of particle filters with and without importance weights.

How to implement the Bayes' formula in the age of ML? title How to avoid the curse of dimensionality: Scalability of particle filters with and without importance weights

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.589752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.576683Z digest=sha256:b3094db3dee17641685d968c4e1b0fca63761b829208ddda8e5171dc7bed745f

Observation dbdc3c3f-c0d9-401d-8413-4bfc6947ae02 · outbound

This paper cites title Modern state estimation methods from the viewpoint of the method of least squares.

How to implement the Bayes' formula in the age of ML? title Modern state estimation methods from the viewpoint of the method of least squares

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.572525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.582666Z digest=sha256:4eafb11f4158ad628653389a691d4c705e97268b739f59fa354daebdb0d56723

Observation b29511cd-c695-4f52-9b3b-b6e2d21dae0f · outbound

This paper cites title Topics in propagation of chaos.

How to implement the Bayes' formula in the age of ML? title Topics in propagation of chaos

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-12T20:33:11.588047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:33:11.588047Z digest=sha256:e443f0c0489fe9a5233ce4558c08a406d9d65bf0e064ee73f212341841b0d57e

Observation 0d02b81f-83a2-4e64-a41f-ad49e6580d4d · outbound

This paper cites Inproceedings.

How to implement the Bayes' formula in the age of ML? Inproceedings

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.556797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.593499Z digest=sha256:417d4063590babe9ea9e454f12c329cae98f36649e4922aebaf1901c9e533fc1

Observation 931fa733-3821-4718-9b9b-80f23ef08119 · outbound

This paper cites an unresolved cited work.

How to implement the Bayes' formula in the age of ML? Unresolved cited work

Reference 63

Resolution
unresolved
raw_fallback, observed 2026-08-12T20:33:12.541828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.599000Z digest=sha256:85de3b419bf6cb24c8f060e9f341cc503d3176208b5916349a106281af57b199

Observation 6ffe7a0b-4cfb-4bd6-bff4-251ba8e6b46f · outbound

This paper cites title An optimal transport formulation of the ensemble K alman filter.

How to implement the Bayes' formula in the age of ML? title An optimal transport formulation of the ensemble K alman filter

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.525363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.604790Z digest=sha256:02bb7b77e91c4be8eb5f8bbb59c67263b7ca32ffda8ac9debe0230bb10dee809

Observation e502f714-7b5d-4d67-bba9-dc1080f76125 · outbound

This paper cites Optimal Transportation Methods in Nonlinear Filtering: The feedback particle filter.

How to implement the Bayes' formula in the age of ML? Optimal Transportation Methods in Nonlinear Filtering: The feedback particle filter

Reference 65

Resolution
verified exact
local_arxiv, observed 2026-08-12T20:33:11.856771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.610506Z digest=sha256:fd33721243666eb7b1e0fcba5a302ee9abe01c1fb661706b543c980a491fb65b

Observation 863e5caf-b92c-476c-bc27-28f997d39c7f · outbound

This paper cites title A survey of feedback particle filter and related controlled interacting particle systems ( CIPS ).

How to implement the Bayes' formula in the age of ML? title A survey of feedback particle filter and related controlled interacting particle systems ( CIPS )

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.509218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.615920Z digest=sha256:7b60285e5d4d5759c19872d934d8f9e73042b91cc702ba3c908d2f125fff207e

Observation 90c2387e-56c0-48fa-874c-f867ba1a5c44 · outbound

This paper cites title Diffusion map-based algorithm for gain function approximation in the feedback particle filter.

How to implement the Bayes' formula in the age of ML? title Diffusion map-based algorithm for gain function approximation in the feedback particle filter

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.491361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.621886Z digest=sha256:77e1329e4b894a6a61fb4cde000371033a55c1a2b63a46faaf6c9df838b359af

Observation 45d0cfdf-2203-4498-8a4f-8d7ef3b4e455 · outbound

This paper cites title Observability and nonlinear filtering.

How to implement the Bayes' formula in the age of ML? title Observability and nonlinear filtering

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.473652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.627852Z digest=sha256:ff6678422d1fd6726b9eef5ac1de25bb0f410ce433f53b16153b38037cb32c34

Observation ccad6570-8f15-49d3-a05f-57fc71ef6620 · outbound

This paper cites an unresolved cited work.

How to implement the Bayes' formula in the age of ML? Unresolved cited work

Reference 69

Resolution
unresolved
raw_fallback, observed 2026-08-12T20:33:12.456723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.633921Z digest=sha256:83085b40e50bb71b3b1474b6c748f8b9f2b6fe8d6133ea4a32be3fb96a09aa58

Observation 6e065b85-7daf-4bac-864d-373aef8587c7 · outbound

This paper cites title Topics in optimal transportation , number 58 , publisher American Mathematical Soc.

How to implement the Bayes' formula in the age of ML? title Topics in optimal transportation , number 58 , publisher American Mathematical Soc

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.440750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.639982Z digest=sha256:7b7dd802dc402b5490ebab3acd9c4ddffda24a923d09eb32f3cdf8b8e05d0ccb

Observation 8bc139b6-53b9-4341-84ec-f10cce10f6e9 · outbound

This paper cites title Ensemble data assimilation without perturbed observations.

How to implement the Bayes' formula in the age of ML? title Ensemble data assimilation without perturbed observations

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-12T20:33:11.644775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:33:11.644775Z digest=sha256:6a99c340bd3f8562488b00514052ddfe53f1063fac33c0697fc135d0cede638e

Observation 23487df3-fa1f-4487-9689-544158d459cd · outbound

This paper cites title Feedback particle filter.

How to implement the Bayes' formula in the age of ML? title Feedback particle filter

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T20:33:12.423847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.650279Z digest=sha256:f3dfacb150f1f99480db68cb1331b66119caacc780efe14aca30cd60c02958e1

Observation 7a3fb05d-81ad-4673-a09b-fa42ad0166de · outbound

This paper cites C @nEZ =PZ6' (|tZRoť Ɏ Z-[ 2n>9Z캼w].

How to implement the Bayes' formula in the age of ML? C @nEZ =PZ6' (|tZRoť Ɏ Z-[ 2n>9Z캼w]

Reference 73

Resolution
verified exact
doi, observed 2026-08-12T20:33:11.726127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-12T20:33:11.655782Z digest=sha256:9cb46d7333b5949539a8764b86cef62ad8130730e712b7379be566d6900c7d2f

Pith citing papers

Observation ea8c9fcb-a592-420c-a5b3-cb8cf3675f79 · inbound

Physics-informed neural particle flow for the Bayesian update step cites this paper.

Physics-informed neural particle flow for the Bayesian update step How to implement the Bayes' formula in the age of ML?

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-15T18:46:29.555124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T18:41:31.982850Z digest=sha256:af015192be5fc08727d411f5fe86a0cfcfe099ea11a88f72e0fe9e132e2be5d9