Probabilistic programming. Probabilistic programs [6] are sequential programs, written in languages like C, Java, Scala, or ML, with two added constructs: (1) the ability to draw values at random from probability distributions, and (2) the ability to condition values of variables in a program through observations. For a comprehensive treatment, see [3]. They have a wide range of applications. P...

Over the past decade, the two research areas of probabilistic databases and probabilistic programming have intensively studied the problem of making structured probabilistic inference scalable, but—so far—both areas developed almost independently of one another. While probabilistic databases have focused on describing tractable query classes based on the structure of query plans and data lineag...

This paper considers the Tchebychev distance for a facility location problem with a probabilistic line barrier in the plane. In particular, we develop a mixed-integer nonlinear programming (MINLP) model for this problem that minimizes the total Tchebychev distance between a new facility and the existing facilities. A numerical example is solved to show the validity of the developed model. Becau...

There has been a substantial recent focus on the concept of probabilistic programming [6] towards its positioning as a prominent paradigm for advancing and facilitating the development of machine-learning applications. A probabilisticprogramming language typically consists of two components: a specification of a stochastic process (the prior), and a specification of observations that restrict t...

Many malware authors borrow source code from other authors when creating new malware, or will take an existing piece of malware and modify it for their needs. As a result, malware within a family of malware (i.e., malware that is closely related in function and structure) often exhibit strong parent–child relationships. Determining the nature of these relationships within a family of malware ca...

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.2.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian...

The basics of logic programming, as embodied by Prolog, are generally well-known in the programming language community. However, more advanced techniques, such as tabling, answer subsumption and probabilistic logic programming fail to attract the attention of a larger audience. The cause for the community’s seemingly limited interest lies with the presentation of these features: the literature ...

Preface The past several years have witnessed a tremendous interest in logic-based probabilistic learning as testified by the number of formalisms and systems and their applications. Logic-based probabilistic learning is a multidisciplinary research area that integrates relational or logic formalisms, probabilistic reasoning mechanisms, and machine learning and data mining principles. Logic-bas...