Add to ORKG• In all but trivial cases, analytical posterior unavailable. • Sequential setup is appealing, but most priors are not conjugate. • Approximations (Normal/Laplace) may not be feasible. Practicalities of Bayesian Analysis • In all but trivial cases, analytical posterior unavailable. • Sequential setup is appealing, but most priors are not conjugate. • Approximations (Normal/Laplace) may not be feasible. • MCMC is typically employed. However, MCMC needs to lap repeatedly through the dataset (#laps ≥ length of the chain). Practicalities of Bayesian Analysis • In all but trivial cases, analytical posterior unavailable. • Sequential setup is appealing, but most priors are not conjugate
popular approach to address inference problems where the likelihood function is intractable, or expe...
Bayesian inference in state-space models requires the solution of high-dimensional integrals, which ...
Both sequential Monte Carlo (SMC) methods (a.k.a. ‘particle filters’) and sequential Markov chain Mo...
Bayesian computation crucially relies on Markov chain Monte Carlo (MCMC) al-gorithms. In the case of...
For Bayesian analysis of massive data, Markov chain Monte Carlo (MCMC) techniques often prove infeas...
PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF
We propose a methodology to sample sequentially from a sequence of probability distributions that ar...
Bayesian computation crucially relies on Markov chain Monte Carlo (MCMC) algorithms. In the case of ...
This paper examines methodology for performing Bayesian inference sequentially on a sequence of post...
This paper examines methodology for performing Bayesian inference sequentially on a sequence of post...
An important feature of Bayesian statistics is the opportunity to do sequential inference: The poste...
Sequential Monte Carlo samplers represent a compelling approach to posterior inference in Bayesian m...
An important feature of Bayesian statistics is the opportunity to do sequential inference: the poste...
Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions i...
International audienceIn many problems, complex non-Gaussian and/or nonlinear models are required to...
popular approach to address inference problems where the likelihood function is intractable, or expe...
Bayesian inference in state-space models requires the solution of high-dimensional integrals, which ...
Both sequential Monte Carlo (SMC) methods (a.k.a. ‘particle filters’) and sequential Markov chain Mo...
Bayesian computation crucially relies on Markov chain Monte Carlo (MCMC) al-gorithms. In the case of...
For Bayesian analysis of massive data, Markov chain Monte Carlo (MCMC) techniques often prove infeas...
PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF
We propose a methodology to sample sequentially from a sequence of probability distributions that ar...
Bayesian computation crucially relies on Markov chain Monte Carlo (MCMC) algorithms. In the case of ...
This paper examines methodology for performing Bayesian inference sequentially on a sequence of post...
This paper examines methodology for performing Bayesian inference sequentially on a sequence of post...
An important feature of Bayesian statistics is the opportunity to do sequential inference: The poste...
Sequential Monte Carlo samplers represent a compelling approach to posterior inference in Bayesian m...
An important feature of Bayesian statistics is the opportunity to do sequential inference: the poste...
Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions i...
International audienceIn many problems, complex non-Gaussian and/or nonlinear models are required to...
popular approach to address inference problems where the likelihood function is intractable, or expe...
Bayesian inference in state-space models requires the solution of high-dimensional integrals, which ...
Both sequential Monte Carlo (SMC) methods (a.k.a. ‘particle filters’) and sequential Markov chain Mo...