Markov point processes and their applications /

Markov point processes and their applications /

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Bibliographic Details
Author / Creator:Van Lieshout, M. N. M.
Imprint:London : Imperial College Press, ©2000.
Description:1 online resource (viii, 175 pages) : illustrations
Language:English
Subject:
Markov processes.
Spatial analysis (Statistics)
Stochastic geometry.
Markov processes.
Spatial analysis (Statistics)
Stochastic geometry.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11170294
Hidden Bibliographic Details
ISBN:9781860949760
1860949762
1860940714
9781860940712
Notes:Includes bibliographical references (pages 157-172) and index.
English.
Print version record.
Summary:These days, an increasing amount of information can be obtained in graphical forms, such as weather maps, soil samples, locations of nests in a breeding colony, microscopical slices, satellite images, radar or medical scans and X-ray techniques. "High level" image analysis is concerned with the global interpretation of images, attempting to reduce it to a compact description of the salient features of the scene. This book takes a stochastic approach. It studies Markov object processes, showing that they form a flexible class of models for a range of problems involving the interpretation of spatial data. Applications can be found in statistical physics (under the name of "Gibbs processes"), environmental mapping of diseases, forestry, identification of ore structure in materials science, signal analysis, object recognition, robot vision, and interpretation of images from medical scans or confocal microscopy.
Other form:Print version: Van Lieshout, M.N.M. Markov point processes and their applications. London : Imperial College Press, ©2000 1860940714
Table of Contents:
  • ""Contents""; ""Chapter 1 Point Processes""; ""1.1 Introduction""; ""1.2 Definitions and notation""; ""1.3 Simple point processes""; ""1.4 Finite point processes""; ""1.5 Poisson point processes""; ""1.6 Finite point processes specified by a density""; ""1.7 Campbell and moment measures""; ""1.8 Interior and exterior conditioning""; ""1.8.1 A review of Palm theory""; ""1.8.2 A review of conditional intensities""; ""Chapter 2 Markov Point Processes""; ""2.1 Ripleyâ€?Kelly Markov point processes""; ""2.2 The Hammersleyâ€?Clifford theorem""; ""2.3 Markov marked point processes""
  • ""2.4 Nearest-neighbour Markov point processes""""2.5 Connected component Markov point processes""; ""Chapter 3 Statistical Inference""; ""3.1 Introduction""; ""3.2 The Metropolisâ€?Hastings algorithm""; ""3.3 Conditional simulation""; ""3.4 Spatial birth-and-death processes""; ""3.5 Exact simulation""; ""3.6 Auxiliary variables and the Gibbs sampler""; ""3.7 Maximum likelihood estimation""; ""3.8 Estimation based on the conditional intensity""; ""3.8.1 Takacsâ€?Fiksel estimation""; ""3.8.2 Maximum pseudo-likelihood estimation""; ""3.9 Goodness of fit testing""; ""3.10 Discussion""
  • ""Chapter 4 Applications""""4.1 Modelling spatial patterns""; ""4.2 Pairwise interaction processes""; ""4.3 Area-interaction processes""; ""4.4 Shot noise and quermass-interaction processes""; ""4.5 Morphologically smoothed area-interaction processes""; ""4.6 Hierarchical and transformed processes""; ""4.7 Cluster processes""; ""4.8 Case study""; ""4.8.1 Exploratory analysis""; ""4.8.2 Model fitting""; ""4.9 Interpolation and extrapolation""; ""4.9.1 Model""; ""4.9.2 Posterior sampling""; ""4.9.3 Monotonicity properties and coupling from the past""; ""Bibliography""; ""Index""

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