Add to ORKGThis is a discussion of the work Rue et al. (2009). In order to evaluate the impact of their Gaussian approximation on the marginal posterior we consider here a slightly different albeit standard stochastic volatility model. We propose a pluggin approximation that is readily available, contrary to the mode of the full conditional suggested in Rue et al. (2009). We obtain a straightforward recurrence relations
The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validati...
The results in this thesis are based on applications of the expectation propagation algorithm to app...
In this letter, we consider a variational approximate Bayesian inference framework, latent-space var...
This is a discussion of the work Rue et al. (2009). In order to evaluate the impact of their Gaussi...
A commentary included in the article by Rue, Martino and Chopin Approximate Bayesian inference for ...
Structured additive regression models are perhaps the most commonly used class of models in statisti...
Summary. Structured additive regression models are perhaps the most commonly used class of models in...
This thesis consists of five papers, presented in chronological order. Their content is summarised i...
Latent variable models give the conditional distribution of (α, y) given θ, where θ is a vector of p...
Latent Gaussian models are a common construct in statistical applications where some latent field, w...
The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate...
The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate...
We present a novel method for approximate inference in Bayesian models and regularized risk functio...
A Bayesian inference framework for supervised Gaussian process latent variable models is introduced....
The following full text is a publisher's version. For additional information about this publica...
The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validati...
The results in this thesis are based on applications of the expectation propagation algorithm to app...
In this letter, we consider a variational approximate Bayesian inference framework, latent-space var...
This is a discussion of the work Rue et al. (2009). In order to evaluate the impact of their Gaussi...
A commentary included in the article by Rue, Martino and Chopin Approximate Bayesian inference for ...
Structured additive regression models are perhaps the most commonly used class of models in statisti...
Summary. Structured additive regression models are perhaps the most commonly used class of models in...
This thesis consists of five papers, presented in chronological order. Their content is summarised i...
Latent variable models give the conditional distribution of (α, y) given θ, where θ is a vector of p...
Latent Gaussian models are a common construct in statistical applications where some latent field, w...
The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate...
The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate...
We present a novel method for approximate inference in Bayesian models and regularized risk functio...
A Bayesian inference framework for supervised Gaussian process latent variable models is introduced....
The following full text is a publisher's version. For additional information about this publica...
The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validati...
The results in this thesis are based on applications of the expectation propagation algorithm to app...
In this letter, we consider a variational approximate Bayesian inference framework, latent-space var...