Statistical Inference for Stochastic Processes, 2007
Starting from the definitions and the properties of reinforced renewal processes and reinforced M... more Starting from the definitions and the properties of reinforced renewal processes and reinforced Markov renewal processes, we characterize, via exchangeability and de Finetti’s representation theorem, a prior that consists of a family of Dirichlet distributions on the space of Markov transition matrices and beta-Stacy processes on distribution functions. Then, we show that this family is conjugate and give some estimate results.
Annals of The Institute of Statistical Mathematics, 2011
A reinforced urn process, which induces a prior on the space of mixtures of Bernstein distributio... more A reinforced urn process, which induces a prior on the space of mixtures of Bernstein distributions is introduced. A nonparametric Bayesian model based on this prior is presented: the elicitation is treated and some connections with Dirichlet mixtures are given. In the last part of the article, an MCMC algorithm to compute the predictive distribution is discussed.
Statistical Inference for Stochastic Processes, 2007
Starting from the definitions and the properties of reinforced renewal processes and reinforced M... more Starting from the definitions and the properties of reinforced renewal processes and reinforced Markov renewal processes, we characterize, via exchangeability and de Finetti’s representation theorem, a prior that consists of a family of Dirichlet distributions on the space of Markov transition matrices and beta-Stacy processes on distribution functions. Then, we show that this family is conjugate and give some estimate results.
Annals of The Institute of Statistical Mathematics, 2011
A reinforced urn process, which induces a prior on the space of mixtures of Bernstein distributio... more A reinforced urn process, which induces a prior on the space of mixtures of Bernstein distributions is introduced. A nonparametric Bayesian model based on this prior is presented: the elicitation is treated and some connections with Dirichlet mixtures are given. In the last part of the article, an MCMC algorithm to compute the predictive distribution is discussed.
Statistical Inference for Stochastic Processes, 2007
Starting from the definitions and the properties of reinforced renewal processes and reinforced M... more Starting from the definitions and the properties of reinforced renewal processes and reinforced Markov renewal processes, we characterize, via exchangeability and de Finetti’s representation theorem, a prior that consists of a family of Dirichlet distributions on the space of Markov transition matrices and beta-Stacy processes on distribution functions. Then, we show that this family is conjugate and give some estimate results.
Annals of The Institute of Statistical Mathematics, 2011
A reinforced urn process, which induces a prior on the space of mixtures of Bernstein distributio... more A reinforced urn process, which induces a prior on the space of mixtures of Bernstein distributions is introduced. A nonparametric Bayesian model based on this prior is presented: the elicitation is treated and some connections with Dirichlet mixtures are given. In the last part of the article, an MCMC algorithm to compute the predictive distribution is discussed.
Statistical Inference for Stochastic Processes, 2007
Starting from the definitions and the properties of reinforced renewal processes and reinforced M... more Starting from the definitions and the properties of reinforced renewal processes and reinforced Markov renewal processes, we characterize, via exchangeability and de Finetti’s representation theorem, a prior that consists of a family of Dirichlet distributions on the space of Markov transition matrices and beta-Stacy processes on distribution functions. Then, we show that this family is conjugate and give some estimate results.
Annals of The Institute of Statistical Mathematics, 2011
A reinforced urn process, which induces a prior on the space of mixtures of Bernstein distributio... more A reinforced urn process, which induces a prior on the space of mixtures of Bernstein distributions is introduced. A nonparametric Bayesian model based on this prior is presented: the elicitation is treated and some connections with Dirichlet mixtures are given. In the last part of the article, an MCMC algorithm to compute the predictive distribution is discussed.
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Papers by Paolo Bulla