Paper Citation Record · LEDGER

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study

As of 11 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2501.06022.

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pith.paper-citation-record.v1
2501.06022 v2

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Outbound references

Observation c1979d27-e197-40ae-ac72-e66d75fa1e3c · outbound

This paper cites Cosmological parameters from CMB and other data: A Monte Carlo approach.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Cosmological parameters from CMB and other data: A Monte Carlo approach

Reference 1

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This paper cites Bayes in the sky: Bayesian inference and model selection in cosmology.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Bayes in the sky: Bayesian inference and model selection in cosmology

Reference 2

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Unresolved cited work

Reference 3

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This paper cites Equation of state calculations by fast computing machines.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Equation of state calculations by fast computing machines

Reference 4

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This paper cites Monte Carlo sampling methods using Markov chains and their applications.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Monte Carlo sampling methods using Markov chains and their applications

Reference 5

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Observation 4122afa2-1ef0-4d87-9ef2-43a9cd1395e2 · outbound

This paper cites Nested sampling methods.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Nested sampling methods

Reference 6

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Observation 792535c0-d918-4a3c-a8f4-f80756e14271 · outbound

This paper cites A comparison of Bayesian and frequentist confidence intervals in the presence of a late Universe degeneracy.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study A comparison of Bayesian and frequentist confidence intervals in the presence of a late Universe degeneracy

Reference 7

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This paper cites A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications

Reference 8

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This paper cites Hybrid Monte Carlo.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Hybrid Monte Carlo

Reference 9

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Observation f0eff06b-dd8b-4b3f-af12-f54748a6c717 · outbound

This paper cites Slice sampling.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Slice sampling

Reference 10

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Observation 2c0536f2-c463-4605-94f8-2685f13bec78 · outbound

This paper cites Nested sampling for general Bayesian computation.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Nested sampling for general Bayesian computation

Reference 11

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Observation 9c478f58-87bf-46de-b088-608a46aad28d · outbound

This paper cites PolyChord: Nested sampling for cosmology.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study PolyChord: Nested sampling for cosmology

Reference 12

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This paper cites polychord: Next-generation nested sampling.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study polychord: Next-generation nested sampling

Reference 13

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This paper cites PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python

Reference 14

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This paper cites Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro

Reference 15

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This paper cites The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

Reference 16

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This paper cites emcee: The MCMC Hammer.Publ.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study emcee: The MCMC Hammer.Publ

Reference 17

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Observation 9016449e-878a-4680-8945-dc314da10508 · outbound

This paper cites dynesty: A dynamic nested sampling package for estimating Bayesian posteriors and evidences.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study dynesty: A dynamic nested sampling package for estimating Bayesian posteriors and evidences

Reference 18

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This paper cites dynesty: V2.1.4, jun 2024., https://doi.org/10.5281/zenodo.12537467 accessed at 16.01.2025.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study dynesty: V2.1.4, jun 2024., https://doi.org/10.5281/zenodo.12537467 accessed at 16.01.2025

Reference 19

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This paper cites MultiNest: An efficient and robust Bayesian inference tool for cosmology and particle physics.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study MultiNest: An efficient and robust Bayesian inference tool for cosmology and particle physics

Reference 20

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This paper cites Cosmology intertwined: A review of the particle physics, astrophysics, and cosmology associated with the cosmological tensions and anomalies.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Cosmology intertwined: A review of the particle physics, astrophysics, and cosmology associated with the cosmological tensions and anomalies

Reference 21

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This paper cites Unveiling lens light complexity with a novel multi-Gaussian expansion approach for strong gravitational lensing.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Unveiling lens light complexity with a novel multi-Gaussian expansion approach for strong gravitational lensing

Reference 22

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Observation ca23807e-85a2-47d9-a16e-21e9049317c1 · outbound

This paper cites Constraining neutrino masses with weak-lensing multiscale peak counts.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Constraining neutrino masses with weak-lensing multiscale peak counts

Reference 23

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This paper cites Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts

Reference 24

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This paper cites DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations

Reference 25

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This paper cites Constraining the dark energy models using baryon acoustic oscillations: An approach independent of H0 · rd.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Constraining the dark energy models using baryon acoustic oscillations: An approach independent of H0 · rd

Reference 26

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This paper cites Late-time constraints on interacting dark energy: Analysis independent of H0, rd, and MB.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Late-time constraints on interacting dark energy: Analysis independent of H0, rd, and MB

Reference 27

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Analytic methods for cosmological likelihoods

Reference 28

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study On the effect of the degeneracy among dark energy parameters

Reference 29

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Accelerating universes with scaling dark matter

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This paper cites How many dark energy parameters?Phys.

Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study How many dark energy parameters?Phys

Reference 31

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Neural sampling machine with stochastic synapse allows brain-like learning and inference

Reference 32

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Fast likelihood-free cosmology with neural density estimators and active learning

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Likelihood-free inference with neural compression of DES SV weak lensing map statistics

Reference 34

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study A parallel tempering algorithm for probabilistic sampling and multimodal optimization

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Continuously tempered Hamiltonian Monte Carlo

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Approach to ergodicity in Monte Carlo simulations

Reference 37

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Reducing quasi-ergodic behavior in Monte Carlo simulations by J-walking: Applications to atomic clusters

Reference 38

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Orbital mcmc 2022

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Hamiltonian Monte Carlo for hierarchical models

Reference 40

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study A Geometric Theory of Higher-Order Automatic Differentiation

Reference 41

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Adaptive Monte Carlo augmented with normalizing flows

Reference 42

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Modern Bayesian Sampling Methods for Cosmological Inference: A Comparative Study Marginal Likelihoods from Monte Carlo Markov Chains

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