Criterion choice is such a hard problem in information recovery and in estimation and inference. In the case of inverse problems with noise, can probabilistic laws provide a basis for empirical estimator choice? That is the problem we investigate in this paper. Large Deviations Theory is used to evaluate the choice of estimator in the case of two fundamental situations-problems in modelling dat...

Identifying causal relations among simultaneously acquired signals is an important problem in multivariate time series analysis. For linear stochastic systems Granger proposed a simple procedure called the Granger causality to detect such relations. In this work we consider nonlinear extensions of Granger’s idea and refer to the result as Extended Granger Causality. A simple approach implementi...

Human conditional reasoning is defeasible: people withdraw logically valid conclusions if they are aware of situations (i.e., exceptions) that prevent the consequent of the rule to happen although the antecedent is given. In this paper we investigate defeasible reasoning with quantified rules. In two experiments we rephrased conditionals from the literature (Experiment 1) and rules from penal c...

Using motion capture data has nowadays utterly been adopted by video game creators or virtual reality applications. In a context of interactive applications, adapting those data to new situations or producing variants of those motions are known as non trivial tasks. We propose an original method that produces motions that preserve the statistical properties of a reference motion while ensuring ...

This paper advocates a basic distinction between two epistemic operations called focusing and revision, which can be defined in any, symbolic or numerical, representation framework which is rich enough for acknowledging the difference between factual evidence and generic knowledge. Revision amounts to modifying the generic knowledge when receiving new pieces of generic knowledge (or the factual...

Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of returns. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estim...

In this article, we describe a possibilistic/probabilistic conditional planner called PTLplan. Being inspired by Bacchus and Kabanza’s TLplan, PTLplan is a progressive planner that uses strategic knowledge encoded in a temporal logic to reduce its search space. Actions effects and sensing can be context dependent and uncertain, and the information the planning agent has at each point in time is...

Many analyses of results from multiple diagnostic tests assume the tests are statistically independent conditional on the true disease status of the subject. This assumption may be violated in practice, especially in situations where none of the tests is a perfectly accurate gold standard. Classical inference for models accounting for the conditional dependence between tests requires that resul...

The mlt package implements maximum likelihood estimation in the class of conditional transformation models. Based on a suitable explicit parameterisation of the unconditional or conditional transformation function using infrastructure from package basefun, we show how one can define, estimate and compare a cascade of increasingly complex transformation models in the maximum likelihood framework...

This paper presents axioms for systems of conditional preferences and provides representation results for these systems. It reports on a new way of representing a system of conditional preferences, by means of a system of conditional lexicographic probabilities (SCLP) (cf. Asheim and Perea, Games and Economic Behavior, forthcoming). An SCLP ensures well-defined conditional beliefs representing ...