COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Piecewise Deterministic Markov Processes for transdimensional sampling from flexible Bayesian survival models
Piecewise Deterministic Markov Processes for transdimensional sampling from flexible Bayesian survival modelsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact nobody. SSD - Stochastic systems for anomalous diffusion Flexible survival models have seen increasing popularity for the estimation of mean survival in the presence of a high degree of administrative censoring where survival curves need to be extrapolated beyond final observed event times. This increased flexibility, however, often introduces challenging model selection problems that have limited their wider application. In this talk I will focus on two such models, the polyhazard model and the piecewise exponential model. We introduce new prior structures that allow for the joint inference of parameters and structural quantities. Posterior sampling is achieved using bespoke MCMC schemes based on Piecewise Deterministic Markov Processes that utilise and extend existing methods for these samplers to target transdimensional posterior distributions. This is a joint work with Samuel Livingstone and Gianluca Baio. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsCambridge Cancer Centre seminars Cambridge University Geographical Society Calais Migrant SolidarityOther talksBonded by Apps? Labour Geographies in the age of Algorithmic Despotism Biosynthesis Of Anti-Inflammatory Triterpene Fatty Acid Esters In Calendula Officinalis Branching Brownian motion, branching random walks, and the Fisher-KPP equation in spatially random environment Scaling laws for large time-series models: More data, more parameters A beep in the dark: 120 years of midwife toads in Great Britain Adaptive probabilistic ODE solvers without adaptive memory requirements |