Direct statistical inference for finite Markov jump processes via the matrix exponential

Chris Sherlock ()
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Chris Sherlock: Lancaster University

Computational Statistics, 2021, vol. 36, issue 4, No 21, 2863-2887

Abstract: Abstract Given noisy, partial observations of a time-homogeneous, finite-statespace Markov chain, conceptually simple, direct statistical inference is available, in theory, via its rate matrix, or infinitesimal generator, $${\mathsf {Q}}$$ Q , since $$\exp ({\mathsf {Q}}t)$$ exp ( Q t ) is the transition matrix over time t. However, perhaps because of inadequate tools for matrix exponentiation in programming languages commonly used amongst statisticians or a belief that the necessary calculations are prohibitively expensive, statistical inference for continuous-time Markov chains with a large but finite state space is typically conducted via particle MCMC or other relatively complex inference schemes. When, as in many applications $${\mathsf {Q}}$$ Q arises from a reaction network, it is usually sparse. We describe variations on known algorithms which allow fast, robust and accurate evaluation of the product of a non-negative vector with the exponential of a large, sparse rate matrix. Our implementation uses relatively recently developed, efficient, linear algebra tools that take advantage of such sparsity. We demonstrate the straightforward statistical application of the key algorithm on a model for the mixing of two alleles in a population and on the Susceptible-Infectious-Removed epidemic model.

Keywords: Markov jump process; Likelihood inference; Bayesian inference; Matrix exponential (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-021-01102-6

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