Paper Citation Record · LEDGER

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought

As of 9 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2505.24594.

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

pith.paper-citation-record.v1
2505.24594 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:35:28.980680Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T19:42:37.884343Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T21:37:24.364632Z

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy28
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a32916a5-7722-43fd-bffd-6fc6e4042bab · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought , " * write output.state after.block = add.period write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:25.307772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 069a6139-613f-4ff3-85ce-baf4df201ed3 · outbound

This paper cites write newline.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought write newline

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:35.408519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 47eedf56-65e8-42eb-b656-6a15476485f8 · outbound

This paper cites P., and Gelfand, A.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought P., and Gelfand, A

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:35.082201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e1ec142e-628b-43a8-85c9-dce4067f69c3 · outbound

This paper cites E., Finley, A.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought E., Finley, A

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:34.794901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9e47f8ee-4d5d-4c23-927e-2b927846c70a · outbound

This paper cites D., Newsam, G.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought D., Newsam, G

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:34.601459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8eeb74a7-4cdf-4d3b-842b-21db5be08d05 · outbound

This paper cites and Johannesson, G.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought and Johannesson, G

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:34.498959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e22f2ec9-100f-4b67-a8d0-02bafe0809eb · outbound

This paper cites an unresolved cited work.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:35:34.318259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fb006b22-508a-4c2d-8699-2186b6e48d14 · outbound

This paper cites and Wikle, C.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought and Wikle, C

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:34.086617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0eeffc56-2916-4f2d-8686-21c37a8f18b6 · outbound

This paper cites O., and Gelfand, A.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought O., and Gelfand, A

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:33.936386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7741829e-d810-4726-88d7-04327854d8c3 · outbound

This paper cites O., Hamm, N.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought O., Hamm, N

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:33.784362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3aae1bca-7e94-461c-b7b1-5a9fd6102de5 · outbound

This paper cites J., Anderson-Bergman, C., Lang, D.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought J., Anderson-Bergman, C., Lang, D

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:33.597434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e7bba050-25ac-41fd-a794-f9ba736b8593 · outbound

This paper cites A., Hepler, S.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought A., Hepler, S

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:33.451370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1873476d-0329-4650-bb9a-a468ecd05d35 · outbound

This paper cites Spatio-temporal forecasting for the US Drought Monitor.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought Spatio-temporal forecasting for the US Drought Monitor

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:33.330451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 358bfa47-ee76-4c03-be47-c8c607fc559f · outbound

This paper cites an unresolved cited work.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:35:33.139626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fa4e0835-8c42-41d6-93af-2302ff5d5265 · outbound

This paper cites O., Banerjee, S., and Gelfand, A.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought O., Banerjee, S., and Gelfand, A

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:33.003384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e937407d-fa1e-492d-8cce-b193a7ecde4b · outbound

This paper cites G., and Nychka, D.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought G., and Nychka, D

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:32.851481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1ff05046-5a6c-499f-8132-f6b2930049d5 · outbound

This paper cites J., Datta, A., Finley, A.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought J., Datta, A., Finley, A

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:32.682119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7e591111-233b-4c25-a860-86221f7e74b6 · outbound

This paper cites an unresolved cited work.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:35:32.473599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d755eaab-6746-4122-8f4b-9e5cea42ce94 · outbound

This paper cites B., Buderman, F.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought B., Buderman, F

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:32.291755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e3d40a96-95ba-4095-8d0e-1cd0db93a973 · outbound

This paper cites B., Johnson, D.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought B., Johnson, D

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:32.080828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 61d9496a-271c-4ba9-9d9b-7335c1808ec7 · outbound

This paper cites WFU High Performance Computing Facility.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought WFU High Performance Computing Facility

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:31.792276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6ba5343c-ff93-4a9e-a56e-68ee9c2a99f1 · outbound

This paper cites and Cressie, N.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought and Cressie, N

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:31.631321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0173e83d-1f08-4a84-b440-087f82c50be8 · outbound

This paper cites and Guinness, J.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought and Guinness, J

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:31.494116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 69a1e97c-2c81-4461-9b17-80078893e901 · outbound

This paper cites G., Schervish, M.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought G., Schervish, M

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:31.345618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bb68d7b6-8a6d-4dc9-85fd-458eac7aaa2a · outbound

This paper cites Fully B ayesian hierarchical modelling in two stages, with application to meta-analysis.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought Fully B ayesian hierarchical modelling in two stages, with application to meta-analysis

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:31.174458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 040fad75-eb29-43bf-9b91-8b1678335a69 · outbound

This paper cites E., Lohmann, D., Houser, P.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought E., Lohmann, D., Houser, P

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:31.014465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c4400037-eacd-44e7-babd-5110f075477a · outbound

This paper cites an unresolved cited work.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:35:30.826236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cb25e4d6-45ac-4fec-b37e-dfdc63861b79 · outbound

This paper cites G., and Sun, Y.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought G., and Sun, Y

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:30.616796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 37f4f913-e83f-4ae2-9fe8-11dd96db4b8e · outbound

This paper cites an unresolved cited work.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:35:30.419582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3da16f36-3ce1-4743-9d73-912a65b8dc8c · outbound

This paper cites and Held, L.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought and Held, L

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:30.260508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 203dd004-40e4-4ccd-b70f-42aa6ff259c8 · outbound

This paper cites an unresolved cited work.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:35:30.051543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 02c79584-b823-494d-a998-dbf1ceafa5f0 · outbound

This paper cites M., Groendyke, C., Haran, M., and Liechty, J.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought M., Groendyke, C., Haran, M., and Liechty, J

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:29.879793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 24e1d1ac-247b-4712-bbf4-ce49ba83ed27 · outbound

This paper cites J., and Anderson-Bergman, C.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought J., and Anderson-Bergman, C

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:29.663090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dd937378-91b5-4256-9349-fcba04a1abb1 · outbound

This paper cites Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:29.422241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7ed65ee7-80d3-439e-a5a3-482a15a08551 · outbound

This paper cites and Cressie, N.

Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought and Cressie, N

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:29.180481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation bd32d0f2-a8a5-498f-a2cd-00594c192d1c · inbound

Making Recursive Bayesian Inference Robust cites this paper.

Making Recursive Bayesian Inference Robust Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:37:24.366262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.