Input Modeling with Phase Type Distributions and Markov Models

Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area.

Input Modeling with Phase Type Distributions and Markov Models

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Input Modeling with Phase-Type Distributions and Markov Models
Language: en
Pages: 127
Authors: Peter Buchholz, Jan Kriege, Iryna Felko
Categories: Mathematics
Type: BOOK - Published: 2014-05-20 - Publisher: Springer

Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area. Due to progress made in recent years with respect to new algorithms to generate PH
Phase-type Distributions
Language: en
Pages: 34
Authors: Marcel F. Neuts
Categories: Mathematics
Type: BOOK - Published: 1989 - Publisher:

Books about Phase-type Distributions
Phase Type Distribution
Language: en
Pages:
Authors: Horvath
Categories: Mathematics
Type: BOOK - Published: 2017-04-20 - Publisher: Wiley-Blackwell

Books about Phase Type Distribution
Matrix-Exponential Distributions in Applied Probability
Language: en
Pages: 736
Authors: Mogens Bladt, Bo Friis Nielsen
Categories: Mathematics
Type: BOOK - Published: 2017-05-18 - Publisher: Springer

This book contains an in-depth treatment of matrix-exponential (ME) distributions and their sub-class of phase-type (PH) distributions. Loosely speaking, an ME distribution is obtained through replacing the intensity parameter in an exponential distribution by a matrix. The ME distributions can also be identified as the class of non-negative distributions with
Characterizations of generalized hyperexponential distributions
Language: en
Pages: 48
Authors: R. F. Botta
Categories: Mathematics
Type: BOOK - Published: 1985 - Publisher:

Generalized hyperexponential (GH) distributions are linear combinations of exponential CDFs with mixing parameters (positive and negative) that sum to unity. The denseness of the class GH with respect to the class of all CDFs defined on (0, infinity) is established by showing that a GH distribution can be found that