The logical and algorithmic properties of stable conditional independence (CI) as an alternative structural representation of conditional independence information are investigated...
Independence--the study of what is relevant to a given problem of reasoning--is an important AI topic. In this paper, we investigate several notions of conditional independence in...
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
Pointwise consistent, feasible procedures for estimating contemporaneous linear causal structure from time series data have been developed using multiple conditional independence ...
We investigate probabilistic propositional logic as a way of expressing and reasoning about uncertainty. In contrast to Bayesian networks, a logical approach can easily cope with i...
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
In this paper we present a method of computing the posterior probability of conditional independence of two or more continuous variables from data, examined at several resolutions...
- Power estimation in combinational modules is addressed from a probabilistic point of view. The zero-delay hypothesis is considered and under highly correlated input streams, the ...
The aim of this paper is to survey and brie y discuss various rules of conditioning proposed in the framework of possibility theory as well as various conditional independence rel...
The main goal of this paper is to describe a new semantic for conditional independence in terms of no double counting of uncertain evidence. For ease of exposition, we use probabi...