Probabilistic programming languages (PPLs) have emerged as a transformative tool for expressing complex statistical models and automating inference procedures. By integrating probability theory into ...
Book Abstract: Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at ...
Generative models of tabular data are key in Bayesian analysis, probabilistic machine learning, and fields like econometrics, healthcare, and systems biology. Researchers have developed methods to ...
Book Abstract: Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information by means of probability theory. Probabilistic Logic Programming is at ...
This is the community edition of GenJAX, a probabilistic programming language in development at MIT's Probabilistic Computing Project. We recommend this version for stability, community contributions, ...
Researchers can demonstrate that on some standard computer-vision tasks, short programs -- less than 50 lines long -- written in a probabilistic programming language are competitive with conventional ...
Probabilistic Programming is a way of defining probabilistic models by overloading the operations in standard programming language to have probabilistic meanings. The goal is to specify probabilistic ...
This manuscript presents a technique for solving a multiple-objective probabilistic fractional programming problem with discrete random variables. A multiple-objective probabilistic mathematical model ...
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