Add to ORKGWe present an approximation technique for probabilistic data models with a large number of hidden variables, based on ideas from statistical physics. We give examples for two nontrivial applications. © 2003 Wiley Periodicals, Inc. Key Words: statistical physics; independent component analysis; probabilistic model
Hidden Markov random fields represent a complex hierarchical model, where the hidden latent process ...
Often scientific information on various data generating processes are presented in the from of numer...
The analysis of quantitative data is central to scientific investigation. Probability theory, which ...
We present an approximation technique for probabilistic data models with a large number of hidden va...
We propose a novel framework for approximations to intractable probabilistic models which is based o...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
In this thesis, we provide some new and interesting solutions to problems of computational inference...
In this paper we consider latent variable models and introduce a new U-likelihood concept for estima...
Time series models are ubiquitous in science, arising in any situation where researchers seek to und...
With very large amounts of data, important aspects of statistical analysis may appear largely descri...
Statistical inference aims to quantify the amount of uncertainty in parameters or functions estimate...
This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of r...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
Hidden Markov random fields represent a complex hierarchical model, where the hidden latent process ...
Often scientific information on various data generating processes are presented in the from of numer...
The analysis of quantitative data is central to scientific investigation. Probability theory, which ...
We present an approximation technique for probabilistic data models with a large number of hidden va...
We propose a novel framework for approximations to intractable probabilistic models which is based o...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
In this thesis, we provide some new and interesting solutions to problems of computational inference...
In this paper we consider latent variable models and introduce a new U-likelihood concept for estima...
Time series models are ubiquitous in science, arising in any situation where researchers seek to und...
With very large amounts of data, important aspects of statistical analysis may appear largely descri...
Statistical inference aims to quantify the amount of uncertainty in parameters or functions estimate...
This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of r...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
Hidden Markov random fields represent a complex hierarchical model, where the hidden latent process ...
Often scientific information on various data generating processes are presented in the from of numer...
The analysis of quantitative data is central to scientific investigation. Probability theory, which ...