包含了lms，rls等多种算法. 例如lms： %LMS4 Problem 1.1.2.1 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % sigmax - standard deviation of input % lambdaW, sigmaW - parameters of first-order Markov % processes which generate
Probability and Statistics by Example Volume 2, Markov Chains 2008 Cambridge Probability and Statistics are as much about intuition and problem solving as they are about theorem proving. Because of this, students can find it very difficult to make a
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed syste
Continuous-time Markov decision processes (MDPs), also known as controlled Markov chains, are used for modeling decision-making problems that arise in operations research (for instance, inventory, manufacturing, and queueing systems), computer scien
Edited by Nong Ye, Lawrence Erlbaum Associates, Inc, 2003 Table of Content I: METHODOLOGIES OF DATA MINING 1 Decision Trees 2 Association Rules 3 Artificial Neural Network Models for Data Mining 4 Statistical Analysis of Normal and Abnormal Data 5 B
The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed
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经典的马尔科夫模型 11.1 Introduction Most of our study of probability has dealt with independent trials processes. These processes are the basis of classical probability theory and much of statistics. We have discussed two of the principal theorems for these
A solution method for solving Markov chains for a class of stochastic process algebra terms is presented. The solution technique is based on a reformulation of the underlying continuous-time Markov chain (CTMC) in terms of semi-Markov processes. For
This definitive textbook provides a solid introduction to discrete and continuous stochastic processes, tackling a complex field in a way that instils a deep understanding of the relevant mathematical principles, and develops an intuitive grasp of t
Hidden Markov processes (HMPs) were introduced into the statistics literature as far back as 1966 . Starting in the mid 1970’s , HMPs have been used in speech recognition, which is perhaps the earliest application of HMPs in a non-mathematical conte
1999_tsitsiklis_Optimal stopping of Markov processes Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives
In this paper we investigate distance functions on finite state Markov processes thatmeasure the behavioural similarity of non-bisimilar processes. We consider both probabilistic bisimilarity metrics, and tracebased distances derived from standard Lp
