1.1 Introduction In a rational programming language, a program specifes a situation encountered by an agent; evaluating the program amounts to computing what a rational agent would believe or do in the situation. Rational programming combines the advantages of declarative representations with features of programming languages such as modularity, compositionality, and type systems. A system desi...

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...

This paper describes a probabilistic model building genetic programming (PMBGP) developed based on the extended compact genetic algorithm (eCGA). Unlike traditional genetic programming, which use fixed recombination operators, the proposed PMBGA adapts linkages. The proposed algorithms, called the extended compact genetic programming (eCGP) adaptively identifies and exchanges non-overlapping bu...

This paper considers a probabilistic inventory model with uniform leadtime demand and fuzzy cost components under probabilistic and imprecise constraints. Firstly we solve the model by general fuzzy non-linear programming technique. Then intuitionistic fuzzy optimization technique is applied and finally, regarding the optimization of the objective function a comparative study is presented among...

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and eecient 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-linear...

Abstract We study the foundations of variational inference, which frames posterior inference as an optimisation problem, for probabilistic programming. The dominant approach in practice is stochastic gradient descent. In particular, a variant using so-called reparameterisation estimator exhibits fast convergence traditional statistics setting. Unfortunately, discontinuities, are readily express...

A major trend in academia and data science is the rapid adoption of Bayesian statistics for analysis modeling, leading to development probabilistic programming languages (PPL). PPL provides a framework that allows users easily specify model perform inference automatically. PyAutoFit Python-based which interfaces with all aspects modeling (e.g., model, data, fitting procedure, visualization, res...

Application of the Minimum Description Length principle to optimization queries in probabilistic programming was investigated on the example of the C++ probabilistic programming library under development. It was shown that incorporation of this criterion is essential for optimization queries to behave similarly to more common queries performing sampling in accordance with posterior distribution...