Pearl [2000, p. 26] attributes to Laplace [1814] the idea of a probabilistic model as a deterministic system with stochastic inputs. Pearl defines causal models in terms of deterministic systems with stochastic inputs. In this paper, I show how deterministic systems with (independent) probabilistic inputs can also be seen as the basis of modern probabilistic programming languages. Probabilistic...

It is time-consuming and error-prone to implement inference procedures for each new probabilistic model. Probabilistic programming addresses this problem by allowing a user to specify the model and having a compiler automatically generate an inference procedure for it. For this approach to be practical, it is important to generate inference code that has reasonable performance. In this paper, w...

Answer Set Programming (ASP) is a powerful declarative computing paradigm that is especially suitable for modeling commonsense reasoning problems. However, the crisp nature of the underlying semantics, the stable model semantics, makes it difficult to handle reasoning domains involving probability and inconsistency. To address this issue, we present an extension of logic programs under the stab...

This paper develops a theory of probabilistic models for risky choices. Part of this theory can be viewed as an extension of the expected utility theory to account for bounded rationality. One probabilistic version of the Archimedean Axiom and two versions of the Independence Axiom are proposed. In addition, additional axioms are proposed of which one is Luce’s Independence from Irrelevant Alte...

Of all scientiic investigations into reasoning with uncertainty and chance, probability theory is perhaps the best understood paradigm. Nevertheless, all studies conducted thus far into the semantics of quantitative logic programming(cf.) have restricted themselves to non-probabilistic semantical characterizations. In this paper, we take a few steps towards rectifying this situation. We deene a...

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...

in this paper semi-markov models are used to forecast the triple dimensions of next earthquake occurrences. each earthquake can be investigated in three dimensions including temporal, spatial and magnitude. semi-markov models can be used for earthquake forecasting in each arbitrary area and each area can be divided into several zones. in semi-markov models each zone can be considered as a state...