Add to ORKGIn this paper, we adopt a nonparametric Bayesian approach and investigate the asymptotic behavior of the posterior distribution in continuous time and general state space semi-Markov processes. In particular, we obtain posterior concentration rates for semi-Markov kernels. For the purposes of this study, we construct robust statistical tests between Hellinger balls around semi-Markov kernels and present some specifications to particular cases, including discrete-time semi-Markov processes and finite state space Markov processes. The objective of this paper is to provide sufficient conditions on priors and semi-Markov kernels that enable us to establish posterior concentration rates
International audienceWe consider finite state space stationary hidden Markov models (HMMs) in the s...
Inference for continuous time multi-state models presents considerable computational difficulties wh...
Abstract. In this paper, we investigate the asymptotic behaviour of the posterior distribution in hi...
In this paper, we adopt a nonparametric Bayesian approach and investigate the asymptotic behavior of...
The development of statistical inference procedures for semi- Markov processes seems to be rather sc...
We consider the asymptotic behaviour of posterior distributions based on continuous observations fro...
In this paper, we review some recent results obtained in the context of Bayesian non and semiparamet...
In this paper we provide general conditions to check on the model and the prior to derive posterior ...
In the paper the semiparametric Markov process model is considered. This model describes the effect ...
In this paper, we consider the well known problem of estimating a density function under qualitative...
We consider finite state space stationary hidden Markov models (HMMs) in the situation where the num...
In this paper we study posterior consistency for different topologies on the parameters for hidden M...
In this paper we provide general conditions to check on the model and the prior to derive posterior ...
International audienceWe consider finite state space stationary hidden Markov models (HMMs) in the s...
Inference for continuous time multi-state models presents considerable computational difficulties wh...
Abstract. In this paper, we investigate the asymptotic behaviour of the posterior distribution in hi...
In this paper, we adopt a nonparametric Bayesian approach and investigate the asymptotic behavior of...
The development of statistical inference procedures for semi- Markov processes seems to be rather sc...
We consider the asymptotic behaviour of posterior distributions based on continuous observations fro...
In this paper, we review some recent results obtained in the context of Bayesian non and semiparamet...
In this paper we provide general conditions to check on the model and the prior to derive posterior ...
In the paper the semiparametric Markov process model is considered. This model describes the effect ...
In this paper, we consider the well known problem of estimating a density function under qualitative...
We consider finite state space stationary hidden Markov models (HMMs) in the situation where the num...
In this paper we study posterior consistency for different topologies on the parameters for hidden M...
In this paper we provide general conditions to check on the model and the prior to derive posterior ...
International audienceWe consider finite state space stationary hidden Markov models (HMMs) in the s...
Inference for continuous time multi-state models presents considerable computational difficulties wh...
Abstract. In this paper, we investigate the asymptotic behaviour of the posterior distribution in hi...