Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations
We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as thes...
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creator | Georgoulas, Anastasis Hillston, Jane Sanguinetti, Guido |
description | We consider continuous time Markovian processes where populations of
individual agents interact stochastically according to kinetic rules. Despite
the increasing prominence of such models in fields ranging from biology to
smart cities, Bayesian inference for such systems remains challenging, as these
are continuous time, discrete state systems with potentially infinite
state-space. Here we propose a novel efficient algorithm for joint state /
parameter posterior sampling in population Markov Jump processes. We introduce
a class of pseudo-marginal sampling algorithms based on a random truncation
method which enables a principled treatment of infinite state spaces. Extensive
evaluation on a number of benchmark models shows that this approach achieves
considerable savings compared to state of the art methods, retaining accuracy
and fast convergence. We also present results on a synthetic biology data set
showing the potential for practical usefulness of our work. |
doi_str_mv | 10.48550/arxiv.1509.08327 |
format | Article |
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individual agents interact stochastically according to kinetic rules. Despite
the increasing prominence of such models in fields ranging from biology to
smart cities, Bayesian inference for such systems remains challenging, as these
are continuous time, discrete state systems with potentially infinite
state-space. Here we propose a novel efficient algorithm for joint state /
parameter posterior sampling in population Markov Jump processes. We introduce
a class of pseudo-marginal sampling algorithms based on a random truncation
method which enables a principled treatment of infinite state spaces. Extensive
evaluation on a number of benchmark models shows that this approach achieves
considerable savings compared to state of the art methods, retaining accuracy
and fast convergence. We also present results on a synthetic biology data set
showing the potential for practical usefulness of our work.</description><identifier>DOI: 10.48550/arxiv.1509.08327</identifier><language>eng</language><subject>Statistics - Machine Learning</subject><creationdate>2015-09</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/1509.08327$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1509.08327$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Georgoulas, Anastasis</creatorcontrib><creatorcontrib>Hillston, Jane</creatorcontrib><creatorcontrib>Sanguinetti, Guido</creatorcontrib><title>Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations</title><description>We consider continuous time Markovian processes where populations of
individual agents interact stochastically according to kinetic rules. Despite
the increasing prominence of such models in fields ranging from biology to
smart cities, Bayesian inference for such systems remains challenging, as these
are continuous time, discrete state systems with potentially infinite
state-space. Here we propose a novel efficient algorithm for joint state /
parameter posterior sampling in population Markov Jump processes. We introduce
a class of pseudo-marginal sampling algorithms based on a random truncation
method which enables a principled treatment of infinite state spaces. Extensive
evaluation on a number of benchmark models shows that this approach achieves
considerable savings compared to state of the art methods, retaining accuracy
and fast convergence. We also present results on a synthetic biology data set
showing the potential for practical usefulness of our work.</description><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz81KxDAUhuFsXMjoBbgyN9CaJjlNu9TBn5ERB6m4LKfNKQSnSUlsce5erK6-zcsHD2NXhch1BSBuMH67JS9A1LmolDTn7OPddw4TWX6HJ0oOPd_5gSL5nvgQIj-EaT7ilwuev2D8DAt_nseJH2LoKSVKfHHI39DbMPImzr5f23TBzgY8Jrr83w1rHu6b7VO2f33cbW_3GZbGZNrojqgvhBRVR6BrQ1JXBADSkBJCSGmRutqC0EqWtjAl1oUGRAVy6EFt2PXf7Sprp-hGjKf2V9iuQvUDrGpLrQ</recordid><startdate>20150928</startdate><enddate>20150928</enddate><creator>Georgoulas, Anastasis</creator><creator>Hillston, Jane</creator><creator>Sanguinetti, Guido</creator><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20150928</creationdate><title>Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations</title><author>Georgoulas, Anastasis ; Hillston, Jane ; Sanguinetti, Guido</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a677-474beec10208be5497e248e55527e300022daeb9d504326d176a9145aa352fc53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Georgoulas, Anastasis</creatorcontrib><creatorcontrib>Hillston, Jane</creatorcontrib><creatorcontrib>Sanguinetti, Guido</creatorcontrib><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Georgoulas, Anastasis</au><au>Hillston, Jane</au><au>Sanguinetti, Guido</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations</atitle><date>2015-09-28</date><risdate>2015</risdate><abstract>We consider continuous time Markovian processes where populations of
individual agents interact stochastically according to kinetic rules. Despite
the increasing prominence of such models in fields ranging from biology to
smart cities, Bayesian inference for such systems remains challenging, as these
are continuous time, discrete state systems with potentially infinite
state-space. Here we propose a novel efficient algorithm for joint state /
parameter posterior sampling in population Markov Jump processes. We introduce
a class of pseudo-marginal sampling algorithms based on a random truncation
method which enables a principled treatment of infinite state spaces. Extensive
evaluation on a number of benchmark models shows that this approach achieves
considerable savings compared to state of the art methods, retaining accuracy
and fast convergence. We also present results on a synthetic biology data set
showing the potential for practical usefulness of our work.</abstract><doi>10.48550/arxiv.1509.08327</doi><oa>free_for_read</oa></addata></record> |
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subjects | Statistics - Machine Learning |
title | Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations |
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