We propose a technique for increasing the efficiency of gradient-based inference and learning in Bayesian networks with multiple layers of continuous latent vari-ables. We show that, in many cases, it is possible to express such models in an auxiliary form, where continuous latent variables are conditionally deterministic given their parents and a set of independent auxiliary variables. Variables of mod-els in this auxiliary form have much larger Markov blankets, leading to significant speedups in gradient-based inference, e.g. rapid mixing Hybrid Monte Carlo and efficient gradient-based optimization. The relative efficiency is confirmed in ex-periments. 1 Introduction and related work Bayesian networks (also called belief networks) are pro...
EDML is a recently proposed algorithm for learning parameters in Bayesian networks. It was originall...
Defence is held on 24.11.2021 12:00 – 16:00 Zoom, https://aalto.zoom.us/j/6031768727Bayesian stat...
AbstractIn recent years, Bayesian networks with a mixture of continuous and discrete variables have ...
. Probabilistic networks (also known as Bayesian belief networks) allow a compact description of com...
Hierarchical Bayesian networks and neural networks with stochastic hidden units are commonly perceiv...
We show how to use a variational approximation to the logistic function to perform approximate infer...
Probabilistic context-free grammars (PCFGs) and dynamic Bayesian networks (DBNs) are widely used seq...
Probabilistic networks, which provide compact descriptions of complex stochastic relationships among...
AbstractIn the construction of a Bayesian network, it is always assumed that the variables starting ...
Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a co...
Bayesian network models are widely used for supervised prediction tasks such as classi cation. Usua...
One of the most important foundational challenge of Statistical relational learning is the developme...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Probabilistic graphical models, e.g. Bayesian Networks, have been traditionally introduced to model ...
In this paper we discuss some practical issues that arise in solv-ing hybrid Bayesian networks that ...
EDML is a recently proposed algorithm for learning parameters in Bayesian networks. It was originall...
Defence is held on 24.11.2021 12:00 – 16:00 Zoom, https://aalto.zoom.us/j/6031768727Bayesian stat...
AbstractIn recent years, Bayesian networks with a mixture of continuous and discrete variables have ...
. Probabilistic networks (also known as Bayesian belief networks) allow a compact description of com...
Hierarchical Bayesian networks and neural networks with stochastic hidden units are commonly perceiv...
We show how to use a variational approximation to the logistic function to perform approximate infer...
Probabilistic context-free grammars (PCFGs) and dynamic Bayesian networks (DBNs) are widely used seq...
Probabilistic networks, which provide compact descriptions of complex stochastic relationships among...
AbstractIn the construction of a Bayesian network, it is always assumed that the variables starting ...
Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a co...
Bayesian network models are widely used for supervised prediction tasks such as classi cation. Usua...
One of the most important foundational challenge of Statistical relational learning is the developme...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Probabilistic graphical models, e.g. Bayesian Networks, have been traditionally introduced to model ...
In this paper we discuss some practical issues that arise in solv-ing hybrid Bayesian networks that ...
EDML is a recently proposed algorithm for learning parameters in Bayesian networks. It was originall...
Defence is held on 24.11.2021 12:00 – 16:00 Zoom, https://aalto.zoom.us/j/6031768727Bayesian stat...
AbstractIn recent years, Bayesian networks with a mixture of continuous and discrete variables have ...