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- Alessio Benavoli, Luigi Chisci, Alfonso Farina
- IEEE Transactions on Signal Processing
- 2007

We address the problem of estimating an unknown parameter vector x in a linear model y=Cx+v subject to the a priori information that the true parameter vector x belongs to a known convex polytope X. The proposed estimator has the parametrized structure of the maximum a posteriori probability (MAP) estimator with prior Gaussian distribution, whose mean and… (More)

- Alessio Benavoli, Marco Zaffalon, Enrique Miranda
- IEEE Trans. Automat. Contr.
- 2011

The classical filtering problem is re-examined to take into account imprecision in the knowledge about the probabilistic relationships involved. Imprecision is modeled in this paper by closed convex sets of probabilities. We derive a solution of the state estimation problem under such a framework that is very general: it can deal with any closed convex set… (More)

- Giorgio Battistelli, Alessio Benavoli, Luigi Chisci
- Automatica
- 2012

This paper deals with the problem of estimating the state of a discrete-time linear stochastic dynamical system on the basis of data collected from multiple sensors subject to a limitation on the communication rate from the sensors. More specifically, the attention is devoted to a centralized sensor network consisting of: (1) multiple remote nodes which… (More)

This paper proposes a model of prior ignorance about a scalar variable based on a set of distributions M. In particular, a set of minimal properties that a set M of distributions should satisfy to be a model of prior ignorance without producing vacuous inferences is defined. In the case the likelihood model corresponds to a one-parameter exponential family… (More)

- Alessio Benavoli, Luigi Chisci, Alfonso Farina
- Signal Processing
- 2009

A connection between the Kalman filter and the Fibonacci sequence is developed. More precisely it is shown that, for a scalar random walk system in which the two noise sources (process and measurement noise) have equal variance, the Kalman filter’s estimate turns out to be a convex linear combination of the a priori estimate and of the measurements with… (More)

- Alessio Benavoli, Branko Ristic, Alfonso Farina, Martin Oxenham, Luigi Chisci
- 2007 10th International Conference on Information…
- 2007

The paper develops an information fusion system that aims at supporting a commander's decision making by providing an assessment of threat, that is an estimate of the extent to which an enemy platform poses a threat based on evidence about its intent and capability. Threat is modelled in the framework of the valuation-based system (VBS), by a network of… (More)

- Francesca Mangili, Alessio Benavoli
- Int. J. Approx. Reasoning
- 2015

The aim of this paper is to derive new near-ignorance models on the probability simplex, which do not directly involve the Dirichlet distribution and, thus, are alternative to the Imprecise Dirichlet Model (IDM). We focus our investigation on a particular class of distributions on the simplex which is known as the class of Normalized Infinitely Divisible… (More)

The problem of aggregating two or more sources of information containing knowledge about a same domain is considered. We propose an aggregation rule for the case where the available information is modeled by coherent lower previsions, corresponding to convex sets of probability mass functions. The consistency between aggregated beliefs and sources of… (More)

Bayesian methods are ubiquitous in machine learning. Nevertheless, the analysis of empirical results is typically performed by frequentist tests. This implies dealing with null hypothesis significance tests and p-values, even though the shortcomings of such methods are well known. We propose a nonparametric Bayesian version of the Wilcoxon signed-rank test… (More)

- Alessio Benavoli, Giorgio Corani, Francesca Mangili
- Journal of Machine Learning Research
- 2016

The statistical comparison of multiple algorithms over multiple data sets is fundamental in machine learning. This is typically carried out by the Friedman test. When the Friedman test rejects the null hypothesis, multiple comparisons are carried out to establish which are the significant differences among algorithms. The multiple comparisons are usually… (More)