Stein’s method meets computational statistics: a review of some recent developments
Stein’s method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein’s method has led to significant advances in computational statistics in recent years. The...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Institute of Mathematical Statistics
2022
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| _version_ | 1797109432247648256 |
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| author | Anastasiou, A Barp, A Briol, F-X Ebner, B Gaunt, RE Ghaderinezhad, F Gorham, J Gretton, A Ley, C Liu, Q Mackey, L Oates, CJ Reinert, G Swan, Y |
| author_facet | Anastasiou, A Barp, A Briol, F-X Ebner, B Gaunt, RE Ghaderinezhad, F Gorham, J Gretton, A Ley, C Liu, Q Mackey, L Oates, CJ Reinert, G Swan, Y |
| author_sort | Anastasiou, A |
| collection | OXFORD |
| description | Stein’s method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein’s method has led to significant advances in computational statistics in recent years. The goal of this survey is to bring together some of these recent developments, and in doing so, to stimulate further research into the successful field of Stein’s method and statistics. The topics we discuss include tools to benchmark and compare sampling methods such as approximate Markov chain Monte Carlo, deterministic alternatives to sampling methods, control variate techniques, parameter estimation and goodness-of-fit testing. |
| first_indexed | 2024-03-07T07:41:48Z |
| format | Journal article |
| id | oxford-uuid:00fe3f7e-d328-495f-80c1-f5e174979fa5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:41:48Z |
| publishDate | 2022 |
| publisher | Institute of Mathematical Statistics |
| record_format | dspace |
| spelling | oxford-uuid:00fe3f7e-d328-495f-80c1-f5e174979fa52023-05-02T11:15:34ZStein’s method meets computational statistics: a review of some recent developmentsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:00fe3f7e-d328-495f-80c1-f5e174979fa5EnglishSymplectic ElementsInstitute of Mathematical Statistics2022Anastasiou, ABarp, ABriol, F-XEbner, BGaunt, REGhaderinezhad, FGorham, JGretton, ALey, CLiu, QMackey, LOates, CJReinert, GSwan, YStein’s method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein’s method has led to significant advances in computational statistics in recent years. The goal of this survey is to bring together some of these recent developments, and in doing so, to stimulate further research into the successful field of Stein’s method and statistics. The topics we discuss include tools to benchmark and compare sampling methods such as approximate Markov chain Monte Carlo, deterministic alternatives to sampling methods, control variate techniques, parameter estimation and goodness-of-fit testing. |
| spellingShingle | Anastasiou, A Barp, A Briol, F-X Ebner, B Gaunt, RE Ghaderinezhad, F Gorham, J Gretton, A Ley, C Liu, Q Mackey, L Oates, CJ Reinert, G Swan, Y Stein’s method meets computational statistics: a review of some recent developments |
| title | Stein’s method meets computational statistics: a review of some recent developments |
| title_full | Stein’s method meets computational statistics: a review of some recent developments |
| title_fullStr | Stein’s method meets computational statistics: a review of some recent developments |
| title_full_unstemmed | Stein’s method meets computational statistics: a review of some recent developments |
| title_short | Stein’s method meets computational statistics: a review of some recent developments |
| title_sort | stein s method meets computational statistics a review of some recent developments |
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