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825,952
Variational algorithms for approximate Bayesian inference
, 2003
"... The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coherent way, avoids overfitting problems, and provides a principled basis for selecting between alternative models. Unfortunately the computations required are usually intractable. This thesis presents ..."
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Cited by 430 (8 self)
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a unified variational Bayesian (VB) framework which approximates these computations in models with latent variables using a lower bound on the marginal likelihood. Chapter 1 presents background material on Bayesian inference, graphical models, and propaga-tion algorithms. Chapter 2 forms
Approximate Bayesian Inference for Quantiles
"... Suppose data consist of a random sample from a distribution function FY, which is unknown, and that interest focuses on inferences on θ, a vector of quantiles of FY. When the likelihood function is not fully specified, a posterior density cannot be calculated and Bayesian infer-ence is difficult. Th ..."
Abstract
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Cited by 20 (1 self)
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Suppose data consist of a random sample from a distribution function FY, which is unknown, and that interest focuses on inferences on θ, a vector of quantiles of FY. When the likelihood function is not fully specified, a posterior density cannot be calculated and Bayesian infer-ence is difficult
A family of algorithms for approximate Bayesian inference
, 2001
"... One of the major obstacles to using Bayesian methods for pattern recognition has been its computational expense. This thesis presents an approximation technique that can perform Bayesian inference faster and more accurately than previously possible. This method, "Expectation Propagation," ..."
Abstract
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Cited by 369 (11 self)
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One of the major obstacles to using Bayesian methods for pattern recognition has been its computational expense. This thesis presents an approximation technique that can perform Bayesian inference faster and more accurately than previously possible. This method, "Expectation Propagation
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 ..."
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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.
Expectation Propagation for Approximate Bayesian Inference
"... This paper presents a new deterministic approximation technique in Bayesian networks. This method, “Expectation Propagation, ” unifies two previous techniques: assumed-density filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian n ..."
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This paper presents a new deterministic approximation technique in Bayesian networks. This method, “Expectation Propagation, ” unifies two previous techniques: assumed-density filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian
Approximate Bayesian inference for complex ecosystems
"... All F1000Prime Reports articles are distributed under the terms of the Creative Commons Attribution-Non Commercial License ..."
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All F1000Prime Reports articles are distributed under the terms of the Creative Commons Attribution-Non Commercial License
A Simple Sequential Algorithm for Approximating Bayesian Inference
"... People can apparently make surprisingly sophisticated inductive inferences, despite the fact that there are constraints on cognitive resources that would make performing exact Bayesian inference computationally intractable. What algorithms could they be using to make this possible? We show that a si ..."
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Cited by 7 (2 self)
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simple sequential algorithm, Win-Stay, Lose-Shift (WSLS), can be used to approximate Bayesian inference, and is consistent with human behavior on a causal learning task. This algorithm provides a new way to understand people’s judgments and a new efficient method for performing Bayesian inference.
Approximate Bayesian inference by adaptive quantization of the hypothesis space
- in Proc. 25th Int. Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
, 2005
"... Abstract. We introduce a method for making approximate Bayesian inference based on quantizing the hypothesis space and repartitioning it as observations become available. The method relies on approximating an optimal inference by using a probability distribution for quantized intervals of the unknow ..."
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Cited by 3 (2 self)
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Abstract. We introduce a method for making approximate Bayesian inference based on quantizing the hypothesis space and repartitioning it as observations become available. The method relies on approximating an optimal inference by using a probability distribution for quantized intervals
Implementing approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations: A manual for the inla-program
, 2008
"... Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalised) linear models, (generalised) additive models, smoothing-spline models, state-space models, semiparametric regression, spatial and spatio-temp ..."
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Cited by 293 (20 self)
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-temporal models, log-Gaussian Cox-processes, geostatistical and geoadditive models. In this paper we consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non
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825,952
