Approximate Bayesian Computation for a Class of Time Series Models
In the following article, we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which we mean that one cannot evaluate the likelihood even up to...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jasra, Ajay [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: 2015 The Authors. International Statistical Review © 2015 International Statistical Institute |
|---|
| Schlagwörter: |
observation driven time series |
|---|
| Übergeordnetes Werk: |
Enthalten in: International statistical review - Oxford : Wiley-Blackwell, 1972, 83(2015), 3, Seite 405-435 |
|---|---|
| Übergeordnetes Werk: |
volume:83 ; year:2015 ; number:3 ; pages:405-435 |
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| DOI / URN: |
10.1111/insr.12089 |
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| 520 | |a In the following article, we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which we mean that one cannot evaluate the likelihood even up to a non‐negative unbiased estimate. This paper reviews and develops a class of approximation procedures based upon the idea of ABC, but specifically maintains the probabilistic structure of the original statistical model. This latter idea is useful, in that one can adopt or adapt established computational methods for statistical inference. Several existing results in the literature are surveyed, and novel developments with regards to computation are given. | ||
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10.1111/insr.12089 doi PQ20160617 (DE-627)OLC1967889678 (DE-599)GBVOLC1967889678 (PRQ)c1949-611140ec99a0873bc7fbe39f1a947ef28d79b763ce8a0fb6a64e58019220d6b70 (KEY)0081104120150000083000300405approximatebayesiancomputationforaclassoftimeserie DE-627 ger DE-627 rakwb eng 310 DNB Jasra, Ajay verfasserin aut Approximate Bayesian Computation for a Class of Time Series Models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In the following article, we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which we mean that one cannot evaluate the likelihood even up to a non‐negative unbiased estimate. This paper reviews and develops a class of approximation procedures based upon the idea of ABC, but specifically maintains the probabilistic structure of the original statistical model. This latter idea is useful, in that one can adopt or adapt established computational methods for statistical inference. Several existing results in the literature are surveyed, and novel developments with regards to computation are given. Nutzungsrecht: 2015 The Authors. International Statistical Review © 2015 International Statistical Institute observation driven time series hidden Markov model Approximate Bayesian computation Approximations Bayesian analysis Time series Statistical inference Enthalten in International statistical review Oxford : Wiley-Blackwell, 1972 83(2015), 3, Seite 405-435 (DE-627)129392707 (DE-600)185055-6 (DE-576)014777703 0020-8779 nnns volume:83 year:2015 number:3 pages:405-435 http://dx.doi.org/10.1111/insr.12089 Volltext http://onlinelibrary.wiley.com/doi/10.1111/insr.12089/abstract http://search.proquest.com/docview/1748863217 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4027 GBV_ILN_4125 GBV_ILN_4126 AR 83 2015 3 405-435 |
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In the following article, we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which we mean that one cannot evaluate the likelihood even up to a non‐negative unbiased estimate. This paper reviews and develops a class of approximation procedures based upon the idea of ABC, but specifically maintains the probabilistic structure of the original statistical model. This latter idea is useful, in that one can adopt or adapt established computational methods for statistical inference. Several existing results in the literature are surveyed, and novel developments with regards to computation are given. |
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In the following article, we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which we mean that one cannot evaluate the likelihood even up to a non‐negative unbiased estimate. This paper reviews and develops a class of approximation procedures based upon the idea of ABC, but specifically maintains the probabilistic structure of the original statistical model. This latter idea is useful, in that one can adopt or adapt established computational methods for statistical inference. Several existing results in the literature are surveyed, and novel developments with regards to computation are given. |
| abstract_unstemmed |
In the following article, we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which we mean that one cannot evaluate the likelihood even up to a non‐negative unbiased estimate. This paper reviews and develops a class of approximation procedures based upon the idea of ABC, but specifically maintains the probabilistic structure of the original statistical model. This latter idea is useful, in that one can adopt or adapt established computational methods for statistical inference. Several existing results in the literature are surveyed, and novel developments with regards to computation are given. |
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| title_short |
Approximate Bayesian Computation for a Class of Time Series Models |
| url |
http://dx.doi.org/10.1111/insr.12089 http://onlinelibrary.wiley.com/doi/10.1111/insr.12089/abstract http://search.proquest.com/docview/1748863217 |
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