Stochastic Modeling of Scientific Data
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€235.60
A01=Peter Guttorp
Author_Peter Guttorp
brownian
Brownian Motion
Brownian motion modeling
Brownian Motion Process
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Category=PBT
Category=PBWH
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chain
diffusion processes
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
estimate
field
Gibbs Measure
Gibbs Sampler
hidden Markov models
Hidden Markov Random Field
Iid Random Variables
Ising Model
L10 Firing
likelihood
Marked Point Process
Marked Poisson Process
markov
Markov chains
Markov Random Field
maximum
Minimal State Space
Neyman Scott Process
Ornstein Uhlenbeck Process
Point Process
point process analysis
Pointwise Confidence Bands
poisson
Poisson Cluster Processes
Poisson Process
Primary Arrival
process
random
scientific data modeling
Spatial Point Processes
Stationary Point Processes
Stochastic Differential Equation
Stochastic Integral
stochastic processes in physical sciences
Wright Fisher Model
Product details
- ISBN 9780412992810
- Weight: 870g
- Dimensions: 156 x 234mm
- Publication Date: 01 Aug 1995
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Stochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner. The distinguishing feature of this work is that, in addition to probability theory, it contains statistical aspects of model fitting and a variety of data sets that are either analyzed in the text or used as exercises. Markov chain Monte Carlo methods are introduced for evaluating likelihoods in complicated models and the forward backward algorithm for analyzing hidden Markov models is presented. The strength of this text lies in the use of informal language that makes the topic more accessible to non-mathematicians. The combinations of hard science topics with stochastic processes and their statistical inference puts it in a new category of probability textbooks. The numerous examples and exercises are drawn from astronomy, geology, genetics, hydrology, neurophysiology and physics.
Guttorp\, Peter
