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Bayesian estimation of mixtures with dynamic transitions and known component parameters

Ivan Nagy, Evgenia Suzdaleva, Miroslav Kárný (2011)

Kybernetika

Probabilistic mixtures provide flexible “universal” approximation of probability density functions. Their wide use is enabled by the availability of a range of efficient estimation algorithms. Among them, quasi-Bayesian estimation plays a prominent role as it runs “naturally” in one-pass mode. This is important in on-line applications and/or extensive databases. It even copes with dynamic nature of components forming the mixture. However, the quasi-Bayesian estimation relies on mixing via constant...

Belief functions induced by multimodal probability density functions, an application to the search and rescue problem

P.-E. Doré, A. Martin, I. Abi-Zeid, A.-L. Jousselme, P. Maupin (2010)

RAIRO - Operations Research - Recherche Opérationnelle

In this paper, we propose a new method to generate a continuous belief functions from a multimodal probability distribution function defined over a continuous domain. We generalize Smets' approach in the sense that focal elements of the resulting continuous belief function can be disjoint sets of the extended real space of dimension n. We then derive the continuous belief function from multimodal probability density functions using the least commitment principle. We illustrate the approach on two...

Belief functions induced by multimodal probability density functions, an application to the search and rescue problem

P.-E. Doré, A. Martin, I. Abi-Zeid, A.-L. Jousselme, P. Maupin (2011)

RAIRO - Operations Research

In this paper, we propose a new method to generate a continuous belief functions from a multimodal probability distribution function defined over a continuous domain. We generalize Smets' approach in the sense that focal elements of the resulting continuous belief function can be disjoint sets of the extended real space of dimension n. We then derive the continuous belief function from multimodal probability density functions using the least commitment principle. We illustrate the approach on two...

Bias-variance decomposition in Genetic Programming

Taras Kowaliw, René Doursat (2016)

Open Mathematics

We study properties of Linear Genetic Programming (LGP) through several regression and classification benchmarks. In each problem, we decompose the results into bias and variance components, and explore the effect of varying certain key parameters on the overall error and its decomposed contributions. These parameters are the maximum program size, the initial population, and the function set used. We confirm and quantify several insights into the practical usage of GP, most notably that (a) the...

Bi-directional nearness in a network by AHP (Analytic Hierarchy Process) and ANP (Analytic Network Process)

Kazutomo Nishizawa (2010)

RAIRO - Operations Research

In this paper we study bi-directional nearness in a network based on AHP (Analytic Hierarchy Process) and ANP (Analytic Network Process). Usually we use forward (one-dimensional) direction nearness based on Euclidean distance. Even if the nearest point to i is point j, the nearest point to j is not necessarily point i. Sowe propose the concept of bi-directional nearness defined by AHP'ssynthesizing of weights “for” direction and “from” direction. This concept of distance is a relative distance...

Binary Relations-based Rough Sets – an Automated Approach

Adam Grabowski (2016)

Formalized Mathematics

Rough sets, developed by Zdzisław Pawlak [12], are an important tool to describe the state of incomplete or partially unknown information. In this article, which is essentially the continuation of [8], we try to give the characterization of approximation operators in terms of ordinary properties of underlying relations (some of them, as serial and mediate relations, were not available in the Mizar Mathematical Library [11]). Here we drop the classical equivalence- and tolerance-based models of rough...

Bio-inspired decentralized autonomous robot mobile navigation control for multi agent systems

Alejandro Rodriguez-Angeles, Luis-Fernando Vazquez Chavez (2018)

Kybernetika

This article proposes a decentralized navigation controller for a group of differential mobile robots that yields autonomous navigation, which allows reaching a certain desired position with a specific desired orientation, while avoiding collisions with dynamic and static obstacles. The navigation controller is constituted by two control loops, the so-called external control loop is based on crowd dynamics, it brings autonomous navigation properties to the system, the internal control loop transforms...

Bounds of graph parameters for global constraints

Nicolas Beldiceanu, Thierry Petit, Guillaume Rochart (2006)

RAIRO - Operations Research - Recherche Opérationnelle

This article presents a basic scheme for deriving systematically a filtering algorithm from the graph properties based representation of global constraints. This scheme is based on the bounds of the graph parameters used in the description of a global constraint. The article provides bounds for the most common used graph parameters.

Bounds of graph parameters for global constraints

Nicolas Beldiceanu, Thierry Petit, Guillaume Rochart (2007)

RAIRO - Operations Research

This article presents a basic scheme for deriving systematically a filtering algorithm from the graph properties based representation of global constraints. This scheme is based on the bounds of the graph parameters used in the description of a global constraint. The article provides bounds for the most common used graph parameters.

Building a knowledge base for correspondence analysis.

M.ª Carmen Bravo Llatas (1994)

Qüestiió

This paper introduces a statistical strategy for Correspondence Analysis. A formal description of the choices, actions and decisions taken during data analysis is built. Rules and heuristics have been obtained from the application of this technique to real case studies.The strategy proposed checks suitability of certain types of data matrices for this analysis and also considers a guidance and interpretation of the application of this technique. Some algorithmic-like rules are presented and specific...

Building adaptive tests using Bayesian networks

Jiří Vomlel (2004)

Kybernetika

We propose a framework for building decision strategies using Bayesian network models and discuss its application to adaptive testing. Dynamic programming and A O ☆ algorithm are used to find optimal adaptive tests. The proposed A O ☆ algorithm is based on a new admissible heuristic function.

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