Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bay...

Probabilistic Logic Programming is an effective formalism for encoding problems characterized by uncertainty. Some of these may require the optimization probability values subject to constraints among distributions random variables. Here, we introduce a new class probabilistic logic programs, namely Optimizable Programs, and provide algorithm find best assignment probabilities variables, such t...

In the past few years there has been a lot of work lying at the intersection of probability theory, logic programming and machine learning [14, 18, 13, 9, 6, 1, 11]. This work is known under the names of statistical relational learning [7, 5], probabilistic logic learning [4], or probabilistic inductive logic programming. Whereas most of the existing works have started from a probabilistic lear...

Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed. In the present paper, we start from inductiv...

supply chain network design decisions are among the strategic decisions of supply chain management which play significant role on the efficient performance of the supply chain. however, there are two challenging factors which may have great impact on the supply chain performance. these factors are on the one hand disruptions and their attendant damages and on the other hand uncertain nature of ...

Probabilistic logic programming under the distribution semantics has been very useful in machine learning. However, inference is expensive so machine learning algorithms may turn out to be slow. In this paper we consider a restriction of the language called hierarchical PLP in which clauses and predicates are hierarchically organized. In this case the language becomes truth-functional and infer...

Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We describe the implementation of Anglican and illustrate how its design facilitates both explorative and industrial use of probabilistic programming.

We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for queryi...

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-line...

stochastic approach to vehicle routing problem: development and theories abstract in this article, a chance constrained (ccp) formulation of the vehicle routing problem (vrp) is proposed. the reality is that once we convert some special form of probabilistic constraint into their equivalent deterministic form then a nonlinear constraint generates. knowing that reliable computer software for lar...