How to implement the Bayes' formula in the age of ML? | Paper Citation Record

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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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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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

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

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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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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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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; raw_fallback, observed 2026-08-12T20:33:13.197832Z

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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; raw_fallback, observed 2026-08-12T20:33:13.182825Z

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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; raw_fallback, observed 2026-08-12T20:33:13.167673Z

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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; raw_fallback, observed 2026-08-12T20:33:13.153853Z

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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; doi, observed 2026-08-12T20:33:11.834863Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:13.139080Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:13.124654Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:13.109962Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:13.095316Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:13.079943Z

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This paper cites an unresolved cited work.

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

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-12T20:33:13.064743Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:13.046677Z

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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

Resolution: verified exact; local_arxiv, observed 2026-08-12T20:33:12.164889Z

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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; raw_fallback, observed 2026-08-12T20:33:13.030018Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:13.015096Z

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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

Resolution: verified exact; doi, observed 2026-08-12T20:33:11.818350Z

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This paper cites Inproceedings.

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

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:13.000280Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:12.984911Z

Source-reported events for the cited work

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This paper cites an unresolved cited work.

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

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-12T20:33:12.970086Z

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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

Resolution: verified exact; local_arxiv, observed 2026-08-12T20:33:12.136268Z

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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: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:12.954094Z

Source-reported events for the cited work

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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

Resolution: unresolved; raw_fallback, observed 2026-08-12T20:33:12.939192Z

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Observation 363b9ad0-47e9-439b-9aeb-ae4671de2c00 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:12.924374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4c9e55bb-0c21-4262-98ac-2c333a8deb57 · outbound

This paper cites ( year 2001 ).

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

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:12.909493Z

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Observation 2c4a4aa4-7998-4416-a048-6504e531ceea · outbound

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; raw_fallback, observed 2026-08-12T20:33:12.894478Z

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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: unresolved; no resolver link, observed 2026-08-12T20:33:11.420385Z

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Unavailable: canonical work link unavailable.

Observation 893ed1d7-c98f-4a1b-a2d4-833abf38393c · outbound

This paper cites title Data Assimilation.

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

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:33:12.880092Z

Source-reported events for the cited work

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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: unresolved; raw_fallback, observed 2026-08-12T20:33:12.865096Z

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Observation 6b17003a-c6b2-475e-810c-98276342a440 · outbound

This paper cites title Computational optimal transport and filtering on riemannian manifolds.

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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

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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

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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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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

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Unavailable: canonical work link unavailable.

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 2026-08-19T06:32:44.657259+00:00.

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

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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

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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

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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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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

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Unavailable: canonical work link unavailable.

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

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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

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers