Abstract “Type 1” statements were introduced by Halpern in 1990 with the goal to represent statistical information about a domain of interest. These are form “x% elements share same property”. The recently proposed language PASTA (Probabilistic Answer set programming for STAtistical probabilities) extends Probabilistic Logic Programs under Distribution Semantics and allows definition this type ...

The Apache Spark stack has enabled fast large-scale data processing. Despite a rich library of statistical models and inference algorithms, it does not give domain users the ability to develop their own models. The emergence of probabilistic programming languages has showed the promise of developing sophisticated probabilistic models in a succinct and programmatic way. These frameworks have the...

We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do “compilation of inference” because our method transforms a denotational specification of an infere...

Most approaches to probabilistic logic programming deal with deduction systems and xpoint semantics for programming systems with user-speci ed weights attached to the formulae of the language, i.e, the aim is to connect logical inference and probabilistic inference. However, such a user-speci c determination of weights is not reusable and often complex. In various applications, automatic method...

Probabilistic logic programming has traditionally focused on languages where probabilities or weights are specified or inferred directly, rather than through Bayesian priors. To address this limitation, we propose a probabilistic logic programming language that bridges the gap between logical and probabilistic inference in categorical models with Dirichlet priors. The language is described in t...

Probabilistic Programming (PP) has recently emerged as an effective approach for building complex probabilistic models. Until recently PP was mostly focused on functional programming while now Probabilistic Logic Programming (PLP) forms a significant subfield. In this paper we aim at presenting a quick overview of the features of current languages and systems for PLP. We first present the basic...

A dynamic programming model with time-domain simulations of contingencies is created to find the least-costly operational strategies according a probabilistic criterion selected preventive and corrective actions. The results show that can identify which appear be satisfactory static analysis, but where system response in violating systems requirements. calculates costs related many possible ope...

This paper presents algorithms based on integer programming, both for probabilistic satisfiability and coherence checking. That is, we consider probabilistic assessments for both standard probability measures (Kolmogorovian setup) and full conditional measures (de Finettian coherence setup), and in both cases verify satisfiability/coherence using integer programming. We present empirical evalua...

We describe two central problems in the programmability of approximate systems—assisted approximate programming and dynamic check generation—and argue that they are, in fact, different views on the same underlying challenge. The common underpinning is the problem of statistical inference over probabilistic programs. We suggest research avenues borrowing from probabilistic programming that can h...