Add to ORKGWe consider Bayesian hierarchical models for event history analysis, where the event times are modeled through an underlying diffusion process, which determines the hazard rate. We show how these models can be e±ciently treated by means of Markov chain Monte Carlo techniques
This work consists of two separate parts. In the first part we extend the work on exact simulation o...
National audienceAmongst various mathematical frameworks, multidimensional continuous-time Markov ju...
Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that ...
We consider Bayesian hierarchical models for event history analysis, where the event times are model...
We consider Bayesian hierarchical models for survival analysis, where the survival times are modeled...
Markov jump processes (MJPs) have been used as models in various fields such as disease progression,...
The need to calibrate increasingly complex statistical models requires a persistent effort for furth...
The methodological framework developed and reviewed in this article concerns the unbiased Monte Car...
Diffusion processes are a promising instrument to realistically model the time-continuous evolution ...
The diffusion model is a successful process model for two-choice reaction times. Implementing it in ...
Pieschner S, Fuchs C. Bayesian inference for diffusion processes: using higher-order approximations ...
Several authors (e.g., Brüderl, Diekmann, Yamaguchi) derive hazard rate models of event history anal...
Modelling random dynamical systems in continuous time, diffusion processes are a powerful tool in ma...
Diffusion models provide a natural way to describe dynamic systems that change continuously in time....
We address the problem of parameter estimation for diffusion driven stochastic volatility models thr...
This work consists of two separate parts. In the first part we extend the work on exact simulation o...
National audienceAmongst various mathematical frameworks, multidimensional continuous-time Markov ju...
Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that ...
We consider Bayesian hierarchical models for event history analysis, where the event times are model...
We consider Bayesian hierarchical models for survival analysis, where the survival times are modeled...
Markov jump processes (MJPs) have been used as models in various fields such as disease progression,...
The need to calibrate increasingly complex statistical models requires a persistent effort for furth...
The methodological framework developed and reviewed in this article concerns the unbiased Monte Car...
Diffusion processes are a promising instrument to realistically model the time-continuous evolution ...
The diffusion model is a successful process model for two-choice reaction times. Implementing it in ...
Pieschner S, Fuchs C. Bayesian inference for diffusion processes: using higher-order approximations ...
Several authors (e.g., Brüderl, Diekmann, Yamaguchi) derive hazard rate models of event history anal...
Modelling random dynamical systems in continuous time, diffusion processes are a powerful tool in ma...
Diffusion models provide a natural way to describe dynamic systems that change continuously in time....
We address the problem of parameter estimation for diffusion driven stochastic volatility models thr...
This work consists of two separate parts. In the first part we extend the work on exact simulation o...
National audienceAmongst various mathematical frameworks, multidimensional continuous-time Markov ju...
Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that ...