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A mathematical framework for learning and adaption: (generalized) random systems with complete connections.

Ulrich Herkenrath, Radu Theodorescu (1981)

Trabajos de Estadística e Investigación Operativa

The aim of this paper is to show that the theory of (generalized) random systems with complete connection may serve as a mathematical framework for learning and adaption. Chapter 1 is of an introductory nature and gives a general description of the problems with which one is faced. In Chapter 2 the mathematical model and some results about it are explained. Chapter 3 deals with special learning and adaption models.

A maximum likelihood estimator of an inhomogeneous Poisson point processes intensity using beta splines

Pavel Krejčíř (2000)

Kybernetika

The problem of estimating the intensity of a non-stationary Poisson point process arises in many applications. Besides non parametric solutions, e. g. kernel estimators, parametric methods based on maximum likelihood estimation are of interest. In the present paper we have developed an approach in which the parametric function is represented by two-dimensional beta-splines.

A method constructing density functions: the case of a generalized Rayleigh variable

Viorel Gh. Vodă (2009)

Applications of Mathematics

In this paper we propose a new generalized Rayleigh distribution different from that introduced in Apl. Mat. 47 (1976), pp. 395–412. The construction makes use of the so-called “conservability approach” (see Kybernetika 25 (1989), pp. 209–215) namely, if X is a positive continuous random variable with a finite mean-value E ( X ) , then a new density is set to be f 1 ( x ) = x f ( x ) / E ( X ) , where f ( x ) is the probability density function of X . The new generalized Rayleigh variable is obtained using a generalized form of the exponential...

A method for knowledge integration

Martin Janžura, Pavel Boček (1998)

Kybernetika

With the aid of Markov Chain Monte Carlo methods we can sample even from complex multi-dimensional distributions which cannot be exactly calculated. Thus, an application to the problem of knowledge integration (e. g. in expert systems) is straightforward.

A Metropolis adjusted Nosé-Hoover thermostat

Benedict Leimkuhler, Sebastian Reich (2009)

ESAIM: Mathematical Modelling and Numerical Analysis

We present a Monte Carlo technique for sampling from the canonical distribution in molecular dynamics. The method is built upon the Nosé-Hoover constant temperature formulation and the generalized hybrid Monte Carlo method. In contrast to standard hybrid Monte Carlo methods only the thermostat degree of freedom is stochastically resampled during a Monte Carlo step.

A Milstein-type scheme without Lévy area terms for SDEs driven by fractional brownian motion

A. Deya, A. Neuenkirch, S. Tindel (2012)

Annales de l'I.H.P. Probabilités et statistiques

In this article, we study the numerical approximation of stochastic differential equations driven by a multidimensional fractional Brownian motion (fBm) with Hurst parameter greater than 1/3. We introduce an implementable scheme for these equations, which is based on a second-order Taylor expansion, where the usual Lévy area terms are replaced by products of increments of the driving fBm. The convergence of our scheme is shown by means of a combination of rough paths techniques and error bounds...

Currently displaying 301 – 320 of 10046