Context-Specific Independence in Directed Relational Probabilistic Models and its Influence on the Efficiency of Gibbs Sampling
نویسنده
چکیده
There is currently a large interest in relational probabilistic models. While the concept of context-specific independence (CSI) has been well-studied for models such as Bayesian networks, this is not the case for relational probabilistic models. In this paper we show that directed relational probabilistic models often exhibit CSI by identifying three different sources of CSI in such models (two of which are inherent to the relational character of the models). It is known that CSI can be used to speed up probabilistic inference. In this paper we show how to do this in a general way for approximate inference based on Gibbs sampling. We perform experiments on real-world data to analyze the influence of the three different types of CSI. The results show that exploiting CSI yields speedups of up to an order of magnitude.
منابع مشابه
Approximate inference in probabilistic relational models
Probabilistic Relational Models (PRMs) are a type of directed graphical model used in the setting of statistical relational learning. PRMs are an extension to Bayesian networks, a popular model which assumes independence between observations. A PRM aims to exploit the logical structure that is often present between observations. We present two approximate inference methods for PRMs. First, we p...
متن کاملLeveraging Node Attributes for Incomplete Relational Data
Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed a...
متن کاملBayesian efficiency analysis through individual effects: Hospital cost frontiers
This paper develops Bayesian tools for making inferences about finn-specific inefficiencies in panel data models. We begin by establishing a Bayesian setting in which fixed and random effects models are defined. What distinguishes these classes of models is the marginal prior independence of the effects. We show how such models can be analyzed using Monte Carlo integration or Gibbs sampling. Th...
متن کاملStructure Learning of Probabilistic Relational Models from Incomplete Relational Data
Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational model (PRM) from incomplete relational data. The missing values are filled in randomly at first, and a maximum likelihood tree (MLT) is generated from the complete data sample. Then, Gibb...
متن کاملUsing Bayesian Network Representations for Effective Sampling from Generative Network Models
Bayesian networks (BNs) are used for inference and sampling by exploiting conditional independence among random variables. Context specific independence (CSI) is a property of graphical models where additional independence relations arise in the context of particular values of random variables (RVs). Identifying and exploiting CSI properties can simplify inference. Some generative network model...
متن کامل