6 edition of **Statistical analysis of finite mixture distributions** found in the catalog.

- 323 Want to read
- 37 Currently reading

Published
**1985**
by Wiley in Chichester, New York
.

Written in English

- Mixture distributions (Probability theory)

**Edition Notes**

Statement | D.M. Titterington, A.F.M. Smith, U.E. Makov. |

Series | Wiley series in probability and mathematical statistics. |

Contributions | Smith, A. F. M., Makov, U. E. |

Classifications | |
---|---|

LC Classifications | QA276.7 .T57 1985 |

The Physical Object | |

Pagination | x, 243 p. : |

Number of Pages | 243 |

ID Numbers | |

Open Library | OL3026040M |

ISBN 10 | 0471907634 |

LC Control Number | 85006434 |

Finite mixture densities can be used to model data from populations known or suspected to contain a number of separate subpopulations. Most commonly used are mixture densities with Gaussian (univariate or multivariate) components, but mixtures with other types of component are also increas ingly used to model, for example, survival by: A method for parameter estimation in image classification or segmentation is studied within the statistical frame of finite mixture distributions. The method models an image as a finite mixture.

Mixture models have been around for over years, as an intuitively simple and practical tool for enriching the collection of probability distributions available for modelling data. In this chapter we describe the basic ideas of the subject, present several alternative representations and perspectives on these models, and discuss some of the elements of inference about the unknowns in Cited by: 1. SOME PROBLEMS WITH DATA FROM FINITE MIXTURE DISTRIBUTIONS D. M. Titterington* Technical Summary Report # April ABSTRACT Finite mixture distributions arise in many statistical applications. After the basic definition of mixture distributions, 'many of these.

Finite mixture densities can be used to model data from populations known or suspected to contain a number of separate subpopulations. Most commonly used are mixture densities with Gaussian (univariate or multivariate) components, but mixtures with other types of component are also increas ingly used to model, for example, survival times. This paper gives a general introduction to the Cited by: This is the basic idea behind mixture distributions: the response x that we observe is modeled as a random variable that has some probability p 1 of being drawn from distribution D 1, probability p 2 of being drawn from distribution D 2, and so forth, with probability p n of being drawn from distribution D n, where n is the number of components.

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Gives a complete account of the mathematical structure, statistical analysis, and applications of finite mixture distributions. Direct applications include economics, medicine, remote sensing, sedimentology, and signal detection (pattern recognition). Also describes indirect applications--in outlier models, density estimation, Bayesian and empirical Bayes analysis, and robustness by: In this book, the authors give a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions.

Gives a complete account of the mathematical structure, statistical analysis, and applications of finite mixture distributions. Direct applications include economics, medicine, remote sensing, sedimentology, and signal detection (pattern recognition).

Statistical analysis of finite mixture distributions. [D M Titterington; Adrian F M Smith; U E Makov] -- In this book, the authors give a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions.

In this book, the authors give a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions. This area of statistics is important to a range of disciplines, and it's methodology is attracting interest from researchers in.

CiteSeerX - Scientific documents that cite the following paper: Statistical Analysis of Finite Mixture Distributions. Finite mixture distributions arise in a variety of applications ranging from the length distribution of fish to the content of DNA in the nuclei of liver cells.

The literature surrounding them is large and goes back to the end of the last century when Karl Pearson published his well-known paper on. Introduction. Finite mixture distributions arise in a variety of applications ranging from the length distribution of fish to the content of DNA in the nuclei of liver cells.

The literature surrounding them is large and goes back to the end of the last century when Karl Pearson published his well-known paper on estimating the five parameters in a mixture of two normal distributions.

This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the.

With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its.

Finite mixture distributions arise in a variety of applications ranging from the length distribution of fish to the content of DNA in the nuclei of liver cells. The literature surrounding them is large and goes back to the end of the last century when Karl Pearson published his well-known paper on estimating the five parameters in a mixture of two normal distributions.

The differences between the two mixture distributions that can be pointed out are: (a) The TNW-pdf takes into account the null wind speeds and the WW-pdf, like the W-pdf, does not, (b) As the WW-pdf is a mixture of distributions that are expressed in closed form, notably less calculation time is required than for the by: This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions.

With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the Cited by: Fitting a finite mixture of multiple Poisson distributions.

In order to model this data set, we assumed as a starting model the finite mixture of multiple Poisson distributions. Later on, we also switch to a finite mixture of multiple negative binomial distributions as described in Section Cited by: 1.

The –nite mixture model provides a natural representation of heterogeneity in a –nite number of latent classes It concerns modeling a statistical distribution by a mixture (or weighted sum) of other distributions Finite mixture models are also known as latent class models unsupervised learning models Finite mixture models are closely related to.

Sell Statistical Analysis of Finite Mixture Distributions, by Titterington - ISBN - Ship for free. - Bookbyte. An introduction to mixture models Franck Picard Laboratoire Statistique et G enome UMR CNRS - INRA - Univ.

d’Evry, France Ma The purpose of cluster analysis is to determine the inner structure of clus-tered data when no information other than the observed values is Size: KB. Analysis of finite mixture of distributions: a statistical tool for biological classification problems.

Equihua M(1). Author information: The statistical tool, which is a cluster analysis technique, is presented in a general context for applications in biology. In particular an ecological example is briefly described: the age class Cited by: 5. Statistical Analysis of Finite Mixture Distributions (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section) By D.

Titterington, Adrian F. Smith, U. Makov Gives a complete account of the mathematical structure, statistical analysis, and applications of finite mixture distributions. INTRODUCTION Flexible Method of Modeling. The importance of ﬁnite mixture models in the statistical analysis of data is evident in the ever- increasing rate at which articles on theoretical and practical aspects of mixture models appear in the statistical and general scientiﬁc literature.

Abstract. In this chapter, mixtures of certain discrete distributions will be considered. As with mixtures of normals, Pearson () appears to have been the first to study such distributions in any detail, deriving moment estimators for the parameters in a mixture of binomial density by: To incorporate the mixture distributions into the framework of popular generalized linear models (GLMs), the authors propose to use finite mixture models (FMMs) to analyze insurance loss data.

The regression approach enhances the traditional whole-book distribution analysis by capturing the impact of individual explanatory variables.Given a finite set of probability density functions p 1 (x),p n (x), or corresponding cumulative distribution functions P 1 (x),P n (x) and weights w 1,w n such that w i ≥ 0 and ∑w i = 1, the mixture distribution can be represented by writing either the density, f, or the distribution function, F, as a sum (which in both cases is a convex combination).