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Results 1 - 10
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175
Approximate Bayesian Inference for Survival Models
, 2010
"... Bayesian analysis of time-to-event data, usually called survival analysis, has received increasing attention in the last years. In Cox-type models it allows to use information from the full likelihood instead of from a partial likelihood, so that the baseline hazard function and the model parameters ..."
Abstract
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Cited by 15 (2 self)
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slow at delivering answers. In this paper, we show how a new inferential tool named Integrated Nested Laplace approximations (INLA) can be adapted and applied to many survival models making Bayesian analysis both fast and accurate without having to rely on MCMC based inference.
Lang S: Generalized structured additive regression based on Bayesian P- splines
- Computational Statistics & Data Analysis
"... Generalized additive models (GAM) for modelling nonlinear effects of continuous covariates are now well established tools for the applied statistician. In this paper we develop Bayesian GAM’s and extensions to generalized structured additive regression based on one or two dimensional P-splines as th ..."
Abstract
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Cited by 45 (9 self)
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Generalized additive models (GAM) for modelling nonlinear effects of continuous covariates are now well established tools for the applied statistician. In this paper we develop Bayesian GAM’s and extensions to generalized structured additive regression based on one or two dimensional P-splines
Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models
, 1993
"... Ways of obtaining approximate Bayes factors for generalized linear models are described, based on the Laplace method for integrals. I propose a new approximation which uses only the output of standard computer programs such as GUM; this appears to be quite accurate. A reference set of proper priors ..."
Abstract
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Cited by 151 (28 self)
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Ways of obtaining approximate Bayes factors for generalized linear models are described, based on the Laplace method for integrals. I propose a new approximation which uses only the output of standard computer programs such as GUM; this appears to be quite accurate. A reference set of proper priors
Animal models and Integrated Nested Laplace Approximations
, 2011
"... Animal models are generalized linear mixed model (GLMM) used in evolutionary biology and animal breeding to identify the genetic part of traits. Integrated Nested Laplace Approximation (INLA) is a methodology for making fast non-sampling based Bayesian inference for hierarchical Gaussian Markov mode ..."
Abstract
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Cited by 4 (0 self)
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Animal models are generalized linear mixed model (GLMM) used in evolutionary biology and animal breeding to identify the genetic part of traits. Integrated Nested Laplace Approximation (INLA) is a methodology for making fast non-sampling based Bayesian inference for hierarchical Gaussian Markov
Laplace Propagation
, 2003
"... We present a novel method for approximate inference in Bayesian models and regularized risk functionals. It is based on the propagation of mean and variance derived from the Laplace approximation of conditional probabilities in factorizing distributions, much akin to Minka's Expectation Pro ..."
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Cited by 9 (0 self)
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We present a novel method for approximate inference in Bayesian models and regularized risk functionals. It is based on the propagation of mean and variance derived from the Laplace approximation of conditional probabilities in factorizing distributions, much akin to Minka's Expectation
Fast Bayesian Model Assessment for Nonparametric Additive Regression
, 2013
"... Variable selection techniques for the classical linear regression model have been widely investigated. Variable selection in fully nonparametric and additive regression models have been studied more recently. A Bayesian approach for nonparametric additive regression models is considered, where the f ..."
Abstract
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Cited by 5 (4 self)
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the functions in the additive model are expanded in a B-spline basis and a multivariate Laplace prior is put on the coefficients. Posterior probabilities of models defined by selection of predictors in the working model are computed, using a Laplace approximation method. The prior times the likelihood
Bayesian Additive Regression Trees
, 2005
"... We develop a Bayesian “sum-of-trees ” model where each tree is constrained by a prior to be a weak leaner. Fitting and inference are accomplished via an iterative back-fitting MCMC algorithm. This model is motivated by ensemble methods in general, and boosting algorithms in particular. Like boosting ..."
Abstract
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Cited by 44 (3 self)
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We develop a Bayesian “sum-of-trees ” model where each tree is constrained by a prior to be a weak leaner. Fitting and inference are accomplished via an iterative back-fitting MCMC algorithm. This model is motivated by ensemble methods in general, and boosting algorithms in particular. Like
Bayesian inference for sparse generalized linear models
- In Machine Learning: ECML
, 2007
"... Abstract. We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties such as sparsity or non-negativit ..."
Abstract
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Cited by 11 (4 self)
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Abstract. We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties such as sparsity or non
Approximate Bayesian Inference for Multivariate Stochastic Volatility Models
, 2008
"... In this report we apply Integrated Nested Laplace approximation (INLA) to a series of multivariate stochastic volatility models. These are a useful construct in financial time series analysis and can be formulated as latent Gaussian Markov Random Field (GMRF) models. This popular class of models is ..."
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is characterised by a GMRF as the second stage of the hierarchical structure and a vector of hyperparameters as the third stage. INLA is a new tool for fast, deterministic inference on latent GMRF models which provides very accurate approximations to the posterior marginals of the model. We compare the performance
Fast approximate inference in hybrid Bayesian networks using dynamic discretisation
"... Abstract. We consider inference in a Bayesian network that can consist of a mix of discrete and continuos variables. It is well known that this is a task that cannot be solved in general using a standard inference algorithms based on the junction-tree. A common solution to this problem is to discret ..."
Abstract
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Cited by 1 (1 self)
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Abstract. We consider inference in a Bayesian network that can consist of a mix of discrete and continuos variables. It is well known that this is a task that cannot be solved in general using a standard inference algorithms based on the junction-tree. A common solution to this problem
Results 1 - 10
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175
