Abstract. Previous experimental results have clearly demonstrated the effectiveness of utilizing context-specific independence (CSI) in probabilistic inference. However, CSI is a special case of a more general independence called contextual weak independence (CWI). In this paper, we show how CWI can be utilized for more efficient probabilistic inference. These results are quite significant as they suggest that CWI may play an important role in probabilistic inference.
It is well known that conditional independence can be used to factorize a joint probability into a m...
Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure le...
The goal of the paper is to recall a recently introduced concept of con-ditional independence in evi...
Context-specific independence (CSI) refers to conditional independencies that are true only in speci...
Three kinds of independence are of interest in the context of Bayesian networks, namely conditional ...
Three kinds of independence are of interest in the context of Bayesian networks, namely conditional ...
Three kinds of independence are of interest in the context of Bayesian networks, namely conditional ...
There is currently a large interest in relational probabilistic models. While the concept of context...
Recently there has been some evidence that the numbers in probabilistic inference don't really ...
Bayesian belief networks have grown to prominence because they provide compact representations for m...
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence prope...
A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference...
There is evidence that the numbers in probabilistic inference don't really matter. This paper c...
International audienceContext specific independence (CSI) is an efficient means to capture independe...
International audienceIn this paper we show for the first time that the probabilistic real-time anal...
It is well known that conditional independence can be used to factorize a joint probability into a m...
Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure le...
The goal of the paper is to recall a recently introduced concept of con-ditional independence in evi...
Context-specific independence (CSI) refers to conditional independencies that are true only in speci...
Three kinds of independence are of interest in the context of Bayesian networks, namely conditional ...
Three kinds of independence are of interest in the context of Bayesian networks, namely conditional ...
Three kinds of independence are of interest in the context of Bayesian networks, namely conditional ...
There is currently a large interest in relational probabilistic models. While the concept of context...
Recently there has been some evidence that the numbers in probabilistic inference don't really ...
Bayesian belief networks have grown to prominence because they provide compact representations for m...
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence prope...
A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference...
There is evidence that the numbers in probabilistic inference don't really matter. This paper c...
International audienceContext specific independence (CSI) is an efficient means to capture independe...
International audienceIn this paper we show for the first time that the probabilistic real-time anal...
It is well known that conditional independence can be used to factorize a joint probability into a m...
Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure le...
The goal of the paper is to recall a recently introduced concept of con-ditional independence in evi...