Inference for continuous time non homogeneous multi-state Markovmodels may present considerable computational difficulties when the process isonly observed at discrete time points without additional information about the statetransitions. In fact, the likelihood can be obtained numerically only by solving theChapman-Kolmogorov equations satisfied by the model transition probabilities. Inthis paper we propose to make Bayesian inference bypassing the likelihood calcula-tion by simulating the whole continuous trajectories conditionally on the observedpoints via a Metropolis-Hastings step based on a piecewise homogeneous Markovprocess. A benchmark data set in the multi-state model literature is used to illustratethe resulting inference.

Bayesian inference for discretely observed non-homogeneous Markov processes / Tancredi, Andrea; Barone, Rosario. - (2021), pp. 1038-1043. (Intervento presentato al convegno SIS2021 tenutosi a Pisa).

Bayesian inference for discretely observed non-homogeneous Markov processes

Tancredi andrea;Barone Rosario
2021

Abstract

Inference for continuous time non homogeneous multi-state Markovmodels may present considerable computational difficulties when the process isonly observed at discrete time points without additional information about the statetransitions. In fact, the likelihood can be obtained numerically only by solving theChapman-Kolmogorov equations satisfied by the model transition probabilities. Inthis paper we propose to make Bayesian inference bypassing the likelihood calcula-tion by simulating the whole continuous trajectories conditionally on the observedpoints via a Metropolis-Hastings step based on a piecewise homogeneous Markovprocess. A benchmark data set in the multi-state model literature is used to illustratethe resulting inference.
2021
SIS2021
metropolis-Hastings; multi-state models; panel data; uniformization
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Bayesian inference for discretely observed non-homogeneous Markov processes / Tancredi, Andrea; Barone, Rosario. - (2021), pp. 1038-1043. (Intervento presentato al convegno SIS2021 tenutosi a Pisa).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1560978
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