Efficient Bayesian model selection for coupled hidden Markov models with application to infectious diseases Article Swipe
Jake Carson
,
Trevelyan J. McKinley
,
Peter Neal
,
Simon E. F. Spencer
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2105.11807
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2105.11807
Performing model selection for coupled hidden Markov models (CHMMs) is highly challenging, owing to the large dimension of the hidden state process. Whilst in principle the hidden state process can be marginalized out via forward filtering, in practice the computational cost of doing so increases exponentially with the number of coupled Markov chains, making this approach infeasible in most applications. Monte Carlo methods can be utilized, but despite many remarkable developments in model selection methodology, generic approaches continue to be ill-suited for such high-dimensional problems. Here we develop specialized solutions for CHMMs with weak inter-chain dependencies. Specifically we construct effective proposal distributions for the hidden state process that remain computationally viable as the number of chains increases, and that require little user input or tuning. This methodology is particularly applicable to individual-level infectious disease models characterized as CHMMs, in which each chain represents an individual, and the coupling represents contact between individuals. Since the only significant contacts are between susceptible and infectious individuals, and since multiple infection pathways are often possible, the resulting CHMMs naturally have low inter-chain dependencies. We demonstrate the utility of our methodology with an application to a study of highly pathogenic avian influenza in chickens.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2105.11807
- https://arxiv.org/pdf/2105.11807
- OA Status
- green
- References
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3165310738
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3165310738Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2105.11807Digital Object Identifier
- Title
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Efficient Bayesian model selection for coupled hidden Markov models with application to infectious diseasesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-05-25Full publication date if available
- Authors
-
Jake Carson, Trevelyan J. McKinley, Peter Neal, Simon E. F. SpencerList of authors in order
- Landing page
-
https://arxiv.org/abs/2105.11807Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2105.11807Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2105.11807Direct OA link when available
- Concepts
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Model selection, Selection (genetic algorithm), Bayesian probability, Variable-order Bayesian network, Computer science, Hidden Markov model, Markov model, Machine learning, Bayesian inference, Artificial intelligence, Markov chain, Econometrics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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3Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.coupling | 147 |
| abstract_inverted_index.multiple | 165 |
| abstract_inverted_index.pathways | 167 |
| abstract_inverted_index.practice | 37 |
| abstract_inverted_index.process. | 21 |
| abstract_inverted_index.proposal | 100 |
| abstract_inverted_index.chickens. | 198 |
| abstract_inverted_index.construct | 98 |
| abstract_inverted_index.dimension | 16 |
| abstract_inverted_index.effective | 99 |
| abstract_inverted_index.increases | 44 |
| abstract_inverted_index.infection | 166 |
| abstract_inverted_index.influenza | 196 |
| abstract_inverted_index.naturally | 174 |
| abstract_inverted_index.possible, | 170 |
| abstract_inverted_index.principle | 24 |
| abstract_inverted_index.problems. | 84 |
| abstract_inverted_index.resulting | 172 |
| abstract_inverted_index.selection | 2, 73 |
| abstract_inverted_index.solutions | 89 |
| abstract_inverted_index.utilized, | 65 |
| abstract_inverted_index.Performing | 0 |
| abstract_inverted_index.applicable | 129 |
| abstract_inverted_index.approaches | 76 |
| abstract_inverted_index.filtering, | 35 |
| abstract_inverted_index.ill-suited | 80 |
| abstract_inverted_index.increases, | 116 |
| abstract_inverted_index.infeasible | 56 |
| abstract_inverted_index.infectious | 132, 161 |
| abstract_inverted_index.pathogenic | 194 |
| abstract_inverted_index.remarkable | 69 |
| abstract_inverted_index.represents | 142, 148 |
| abstract_inverted_index.application | 188 |
| abstract_inverted_index.demonstrate | 180 |
| abstract_inverted_index.individual, | 144 |
| abstract_inverted_index.inter-chain | 94, 177 |
| abstract_inverted_index.methodology | 126, 185 |
| abstract_inverted_index.significant | 155 |
| abstract_inverted_index.specialized | 88 |
| abstract_inverted_index.susceptible | 159 |
| abstract_inverted_index.Specifically | 96 |
| abstract_inverted_index.challenging, | 11 |
| abstract_inverted_index.developments | 70 |
| abstract_inverted_index.individuals, | 162 |
| abstract_inverted_index.individuals. | 151 |
| abstract_inverted_index.marginalized | 31 |
| abstract_inverted_index.methodology, | 74 |
| abstract_inverted_index.particularly | 128 |
| abstract_inverted_index.applications. | 59 |
| abstract_inverted_index.characterized | 135 |
| abstract_inverted_index.computational | 39 |
| abstract_inverted_index.dependencies. | 95, 178 |
| abstract_inverted_index.distributions | 101 |
| abstract_inverted_index.exponentially | 45 |
| abstract_inverted_index.computationally | 109 |
| abstract_inverted_index.high-dimensional | 83 |
| abstract_inverted_index.individual-level | 131 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |