Sequential Monte Carlo Methods in the nimble R Package
nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model specification and the ability to program model-generic algorithms. Specifically, the package allows users to code models in the BUGS...
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creator | Michaud, Nicholas de Valpine, Perry Turek, Daniel Paciorek, Christopher J Nguyen, Dao |
description | nimble is an R package for constructing algorithms and conducting inference
on hierarchical models. The nimble package provides a unique combination of
flexible model specification and the ability to program model-generic
algorithms. Specifically, the package allows users to code models in the BUGS
language, and it allows users to write algorithms that can be applied to any
appropriate model. In this paper, we introduce nimble's capabilities for
state-space model analysis using sequential Monte Carlo (SMC) techniques. We
first provide an overview of state-space models and commonly-used SMC
algorithms. We then describe how to build a state-space model and conduct
inference using existing SMC algorithms within nimble. SMC algorithms within
nimble currently include the bootstrap filter, auxiliary particle filter,
ensemble Kalman filter, IF2 method of iterated filtering, and a particle MCMC
sampler. These algorithms can be run in R or compiled into C++ for more
efficient execution. Examples of applying SMC algorithms to linear
autoregressive models and a stochastic volatility model are provided. Finally,
we give an overview of how model-generic algorithms are coded within nimble by
providing code for a simple SMC algorithm. This illustrates how users can
easily extend nimble's SMC methods in high-level code. |
doi_str_mv | 10.48550/arxiv.1703.06206 |
format | Article |
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on hierarchical models. The nimble package provides a unique combination of
flexible model specification and the ability to program model-generic
algorithms. Specifically, the package allows users to code models in the BUGS
language, and it allows users to write algorithms that can be applied to any
appropriate model. In this paper, we introduce nimble's capabilities for
state-space model analysis using sequential Monte Carlo (SMC) techniques. We
first provide an overview of state-space models and commonly-used SMC
algorithms. We then describe how to build a state-space model and conduct
inference using existing SMC algorithms within nimble. SMC algorithms within
nimble currently include the bootstrap filter, auxiliary particle filter,
ensemble Kalman filter, IF2 method of iterated filtering, and a particle MCMC
sampler. These algorithms can be run in R or compiled into C++ for more
efficient execution. Examples of applying SMC algorithms to linear
autoregressive models and a stochastic volatility model are provided. Finally,
we give an overview of how model-generic algorithms are coded within nimble by
providing code for a simple SMC algorithm. This illustrates how users can
easily extend nimble's SMC methods in high-level code.</description><identifier>DOI: 10.48550/arxiv.1703.06206</identifier><language>eng</language><subject>Statistics - Computation</subject><creationdate>2017-03</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1703.06206$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1703.06206$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Michaud, Nicholas</creatorcontrib><creatorcontrib>de Valpine, Perry</creatorcontrib><creatorcontrib>Turek, Daniel</creatorcontrib><creatorcontrib>Paciorek, Christopher J</creatorcontrib><creatorcontrib>Nguyen, Dao</creatorcontrib><title>Sequential Monte Carlo Methods in the nimble R Package</title><description>nimble is an R package for constructing algorithms and conducting inference
on hierarchical models. The nimble package provides a unique combination of
flexible model specification and the ability to program model-generic
algorithms. Specifically, the package allows users to code models in the BUGS
language, and it allows users to write algorithms that can be applied to any
appropriate model. In this paper, we introduce nimble's capabilities for
state-space model analysis using sequential Monte Carlo (SMC) techniques. We
first provide an overview of state-space models and commonly-used SMC
algorithms. We then describe how to build a state-space model and conduct
inference using existing SMC algorithms within nimble. SMC algorithms within
nimble currently include the bootstrap filter, auxiliary particle filter,
ensemble Kalman filter, IF2 method of iterated filtering, and a particle MCMC
sampler. These algorithms can be run in R or compiled into C++ for more
efficient execution. Examples of applying SMC algorithms to linear
autoregressive models and a stochastic volatility model are provided. Finally,
we give an overview of how model-generic algorithms are coded within nimble by
providing code for a simple SMC algorithm. This illustrates how users can
easily extend nimble's SMC methods in high-level code.</description><subject>Statistics - Computation</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj0tuwjAUAL3pAlEOwApfIMF_x8sqagEJRFXYR8_JC7EICQ0Bwe0Rn9XsRjOEjDmLVaI1m0J3DZeYWyZjZgQzA2I2-H_Gpg9Q01Xb9EhT6OqWrrCv2uJEQ0P7CmkTDr5G-kd_Id_DDj_JRwn1CUdvDsn253ubzqPlerZIv5YRGGsiDdKB0S7ngmntEgm-AG99UYI0itlEORQiEYxbLLUC7zW3QtiSO64KLuSQTF7aZ3h27MIBulv2GMieA_IORq8-WQ</recordid><startdate>20170317</startdate><enddate>20170317</enddate><creator>Michaud, Nicholas</creator><creator>de Valpine, Perry</creator><creator>Turek, Daniel</creator><creator>Paciorek, Christopher J</creator><creator>Nguyen, Dao</creator><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20170317</creationdate><title>Sequential Monte Carlo Methods in the nimble R Package</title><author>Michaud, Nicholas ; de Valpine, Perry ; Turek, Daniel ; Paciorek, Christopher J ; Nguyen, Dao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-5a39a659c12055983abdab7bdfa36407849e2282017ef54abb517227f1914d123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Statistics - Computation</topic><toplevel>online_resources</toplevel><creatorcontrib>Michaud, Nicholas</creatorcontrib><creatorcontrib>de Valpine, Perry</creatorcontrib><creatorcontrib>Turek, Daniel</creatorcontrib><creatorcontrib>Paciorek, Christopher J</creatorcontrib><creatorcontrib>Nguyen, Dao</creatorcontrib><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Michaud, Nicholas</au><au>de Valpine, Perry</au><au>Turek, Daniel</au><au>Paciorek, Christopher J</au><au>Nguyen, Dao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sequential Monte Carlo Methods in the nimble R Package</atitle><date>2017-03-17</date><risdate>2017</risdate><abstract>nimble is an R package for constructing algorithms and conducting inference
on hierarchical models. The nimble package provides a unique combination of
flexible model specification and the ability to program model-generic
algorithms. Specifically, the package allows users to code models in the BUGS
language, and it allows users to write algorithms that can be applied to any
appropriate model. In this paper, we introduce nimble's capabilities for
state-space model analysis using sequential Monte Carlo (SMC) techniques. We
first provide an overview of state-space models and commonly-used SMC
algorithms. We then describe how to build a state-space model and conduct
inference using existing SMC algorithms within nimble. SMC algorithms within
nimble currently include the bootstrap filter, auxiliary particle filter,
ensemble Kalman filter, IF2 method of iterated filtering, and a particle MCMC
sampler. These algorithms can be run in R or compiled into C++ for more
efficient execution. Examples of applying SMC algorithms to linear
autoregressive models and a stochastic volatility model are provided. Finally,
we give an overview of how model-generic algorithms are coded within nimble by
providing code for a simple SMC algorithm. This illustrates how users can
easily extend nimble's SMC methods in high-level code.</abstract><doi>10.48550/arxiv.1703.06206</doi><oa>free_for_read</oa></addata></record> |
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subjects | Statistics - Computation |
title | Sequential Monte Carlo Methods in the nimble R Package |
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