This thesis develops new methods for efficient approximate inference in probabilistic models. Such models are routinely used in different fields, yet they remain computationally challenging as they involve high-dimensional integrals. We propose different approximate inference approaches addressing some challenges in probabilistic machine learning and Bayesian statistics. First, we present a Bayesian framework for genome-wide inference of DNA methylation levels and devise an efficient particle filtering and smoothing algorithm that can be used to identify differentially methylated regions between case and control groups. Second, we present a scalable inference approach for state space models by combining variational methods with sequential M...
Computational Bayesian statistics builds approximations to the posterior distribution either bysampl...
While modern machine learning and deep learning seem to dominate the areas where scalability and mod...
The Markov Chain Monte-Carlo (MCMC) born in early 1950s has recently aroused great interest among s...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
Bayesian inference allows to make conclusions based on some antecedents that depend on prior knowled...
Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other...
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesi...
Given a statistical model that attempts to explain the data, calculating the Bayes’ posterior distr...
We present a framework for approximate Bayesian inference when only a limited number of noisy log-li...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
We consider a method for approximate inference in hidden Markov models (HMMs). The method circum-ven...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
constitutes a class of computational methods rooted in Bayesian statistics. In all model-based stati...
Due to the availability of larger data-sets and the complexity of Bayesian statistical models in mod...
Computational Bayesian statistics builds approximations to the posterior distribution either bysampl...
While modern machine learning and deep learning seem to dominate the areas where scalability and mod...
The Markov Chain Monte-Carlo (MCMC) born in early 1950s has recently aroused great interest among s...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
Bayesian inference allows to make conclusions based on some antecedents that depend on prior knowled...
Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other...
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesi...
Given a statistical model that attempts to explain the data, calculating the Bayes’ posterior distr...
We present a framework for approximate Bayesian inference when only a limited number of noisy log-li...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
We consider a method for approximate inference in hidden Markov models (HMMs). The method circum-ven...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
constitutes a class of computational methods rooted in Bayesian statistics. In all model-based stati...
Due to the availability of larger data-sets and the complexity of Bayesian statistical models in mod...
Computational Bayesian statistics builds approximations to the posterior distribution either bysampl...
While modern machine learning and deep learning seem to dominate the areas where scalability and mod...
The Markov Chain Monte-Carlo (MCMC) born in early 1950s has recently aroused great interest among s...