Speeding-up structured probabilistic inference using pattern mining

Speeding-up structured probabilistic inference using pattern mining

In many domains where experts are the main source of knowledge, e.g., in reliability and risk management, a framework well suited for modeling, maintenance and exploitation of complex probabilistic systems is essential. In these domains, models usually define closed-world systems and result from the aggregation of multiple patterns repeated many times. Object Oriented-based Frameworks such as P...

Speeding Up Inference for Probabilistic Logic Programs

Probabilistic Logic Programming (PLP) allows to represent domains containing many entities connected by uncertain relations and has many applications in particular in Machine Learning. PITA is a PLP algorithm for computing the probability of queries that exploits tabling, answer subsumption and Binary Decision Diagrams (BDDs). PITA does not impose any restriction on the programs. Other algorith...

Speeding Up Pattern

Speeding Up Inference in Markovian Models

Sequential statistical models such as dynamic Bayesian networks and hidden Markov models more specifically, model stochastic processes over time. In this paper, we study for these models the effect of consecutive similar observations on the posterior probability distribution of the represented process. We show that, given such observations, the posterior distribution converges to a limit distri...

Speeding up Static Probabilistic Timing Analysis

Probabilistic Timing Analysis (PTA) has emerged recently to derive trustworthy and tight WCET estimates. Computational costs due to the use of the mathematical operator called convolution used by SPTA – the static variant of PTA – and also deployed in many domains including signal and image processing, jeopardize the scalability of SPTA to real-size programs. We evaluate, qualitatively and quan...