Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have defined, developed, and tested a probabilistic programming language intermedi...

1 In [20], a new Hybrid Probabilistic Logic Programs framework is proposed, and a new semantics is developed to enable encoding and reasoning about real-world applications. In this paper, we extend the language of Hybrid Probabilistic Logic Programs framework in [20] to allow non-monotonic negation, and define two alternative semantics: stable probabilistic model semantics and probabilistic wel...

in this research, an integrated inventory problem is formulated for a single-vendor multiple-retailer supply chain that works according to the vendor managed inventory policy. the model is derived based on the economic order quantity in which shortages with penalty costs at the retailers` level is permitted. as predicting customer demand is the most important problem in inventory systems and th...

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying pr...

This paper considers a fuzzy programming approach for a multi-objective single machine scheduling problem when processing times of jobs are normal random variables. The probabilistic problem is converted into an equivalent deterministic programming problem. Then the fuzzy programming technique has been applied to obtain a compromise solution. A numerical example demonstrates the feasibility of ...

Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach for searching structured data based on probabilistic programming and nonparametric Bayes. Users specify queries in a probabilistic language that combines standard SQL database se...

Probabilistic inference can be realized using weighted model counting. Despite a lot of progress, computing weighted model counts exactly is still infeasible for many problems of interest, and one typically has to resort to approximation methods. We contribute a new bounded approximation method for weighted model counting based on probabilistic logic programming principles. Our bounded approxim...

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of and reasoning with complex, structured probability distributions. Examples include functional languages (Church [Goodman et al., 2008], IBAL [Pfeffer, 2001]), object-oriented languages (Figaro [Pfeffer, 2009]), and logic languages (Pr...

Probabilistic predicate transformers provide a semantics for imperative programs containing both demonic and probabilistic nondeterminism. Like the (standard) predicate transformers popularised by Dijkstra, they model programs as functions from final results to the initial conditions sufficient to achieve them. This paper presents practical proof rules, using the probabilistic transformers, for...

This thesis develops probabilistic programming as a productive metaphor for understanding cognition, both with respect to mental representations and the manipulation of such representations. In the first half of the thesis, I demonstrate the representational power of probabilistic programs in the domains of concept learning and social reasoning. I provide examples of richly structured concepts,...