Abstract—In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can be potentially infinite. Moreover, the number of layers can also be infinite. Previous non-parametric Bayesian methods assume binary values for both hidden factors and weights. In contrast, we construct the model structure that allows continuous values for the hidden factors and weights, which makes the model suitable for more applications. We use the Metropolis-Hastings method to infer the model structure. Then the performance of the algorithm is evaluated by the experiments. Simulation results show that the model fits the underlying ...
We present a new parallel algorithm for learning Bayesian inference networks from data. Our learning...
It is a challenging task of learning a large Bayesian network from a small data set. Most convention...
We present new algorithms for learning Bayesian networks from data with missing values using a data ...
We give a polynomial-time algorithm for provably learning the structure and pa-rameters of bipartite...
In this paper, we present a nonparametric Bayesian approach towards one-hidden-layer feed forward ne...
The Bayesian network is a powerful tool for modeling of cause effect and other uncertain relations b...
The objective of this thesis is to design an algorithm for learning the structure of non-parametric ...
Deep belief networks are a powerful way to model complex probability distributions. However, learnin...
Learning from data ranges between extracting essentials from the data, to the more fundamental and v...
This paper considers a parallel algorithm for Bayesian network structure learning from large data se...
This publication offers and investigates efficient Monte Carlo simulation methods in order to realiz...
In this paper, we provide new complexity results for algorithms that learn discretevariable Bayesian...
Early methods for learning a Bayesian network that optimizes a scoring function for a given dataset ...
The conditional independence assumption of naive Bayes essentially ignores attribute dependencies an...
Bayesian networks learned from data and background knowledge have been broadly used to reason under ...
We present a new parallel algorithm for learning Bayesian inference networks from data. Our learning...
It is a challenging task of learning a large Bayesian network from a small data set. Most convention...
We present new algorithms for learning Bayesian networks from data with missing values using a data ...
We give a polynomial-time algorithm for provably learning the structure and pa-rameters of bipartite...
In this paper, we present a nonparametric Bayesian approach towards one-hidden-layer feed forward ne...
The Bayesian network is a powerful tool for modeling of cause effect and other uncertain relations b...
The objective of this thesis is to design an algorithm for learning the structure of non-parametric ...
Deep belief networks are a powerful way to model complex probability distributions. However, learnin...
Learning from data ranges between extracting essentials from the data, to the more fundamental and v...
This paper considers a parallel algorithm for Bayesian network structure learning from large data se...
This publication offers and investigates efficient Monte Carlo simulation methods in order to realiz...
In this paper, we provide new complexity results for algorithms that learn discretevariable Bayesian...
Early methods for learning a Bayesian network that optimizes a scoring function for a given dataset ...
The conditional independence assumption of naive Bayes essentially ignores attribute dependencies an...
Bayesian networks learned from data and background knowledge have been broadly used to reason under ...
We present a new parallel algorithm for learning Bayesian inference networks from data. Our learning...
It is a challenging task of learning a large Bayesian network from a small data set. Most convention...
We present new algorithms for learning Bayesian networks from data with missing values using a data ...