Statistical Inference in Stochastic Processes
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advanced statistical methods
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AIC Criterion
AIC Statistic
Asymptotic Risk
Atypical Orientation
automatic-update
B01=N.U. Prabhu
Category1=Non-Fiction
Category=PBT
COP=United Kingdom
Cumulative Hazard Rate
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Discrete Time Linear Stochastic Systems
edge detection algorithms
Empirical Bayes Estimator
eq_isMigrated=2
eq_nobargain
Gaussian Likelihood
image analysis
inference for diffusion processes with jumps
Kaiman Filter
Kalman-Bucy linear system
Kernel Estimator
Language_English
Linear Volterra Integral Equation
Log Likelihood Function
Markov Random Field
Maximum Likelihood Estimator
Missing Values
Nonparametric Maximum Likelihood Estimators
PA=Temporarily unavailable
Price_€50 to €100
probabalistic models
probability theory
PS=Active
Rainfall Fields
Shrinkage Estimator
Skorohod Space
softlaunch
state-space models
statistical techniques
Stein's Lemma
Stein’s Lemma
stochastic inference
Stochastic Integrals
stochastic modeling
survival data analysis
Type Token Relationship
Unique Stationary Solution
Volterra Integral Equation
Weak Convergence
Product details
- ISBN 9780367403072
- Weight: 453g
- Dimensions: 152 x 229mm
- Publication Date: 17 Oct 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Paperback
- Language: English
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Covering both theory and applications, this collection of eleven contributed papers surveys the role of probabilistic models and statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters that determine the drift and the jump mechanism of a di
N. U. Prabhu, I. V. Basawa
