Bayesian inference for discretely observed continuous time multi-state models
From MaRDI portal
Publication:6628504
DOI10.1002/SIM.9449zbMATH Open1547.62136MaRDI QIDQ6628504
Author name not available (Why is that?), Andrea Tancredi
Publication date: 29 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
Cites Work
- Flexible Nonhomogeneous Markov Models for Panel Observed Data
- Penalised maximum likelihood estimation in multi-state models for interval-censored data
- Estimating parametric semi-Markov models from panel data using phase-type approximations
- Simulation from endpoint-conditioned, continuous-time Markov chains on a finite state space, with applications to molecular evolution
- Semi-Markov Models with Phase-Type Sojourn Distributions
- An Exact Gibbs Sampler for the Markov-Modulated Poisson Process
- Modeling Nonhomogeneous Markov Processes via Time Transformation
- Statistical methods for panel data from a semi-Markov process, with application to HPV
- The Analysis of Panel Data Under a Markov Assumption
- A Markov Model for Analysing Cancer Markers and Disease States in Survival Studies
- Non-Homogeneous Markov Models in the Analysis of Survival After Breast Cancer
- Statistical Models
- An extended likelihood framework for modelling discretely observed credit rating transitions
- Approximate Bayesian inference for discretely observed continuous‐time multi‐state models
- Statistical Inference for Discretely Observed Markov Jump Processes
- Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records
This page was built for publication: Bayesian inference for discretely observed continuous time multi-state models
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6628504)
