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Cambridge series on statistical and probabilistic mathematics

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Cambridge series on statistical and probabilistic mathematics

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Material Type
図書
Publication, Distribution, etc.
Alternative Title
Cambridge series in statistical and probabilistic mathematics
Place of Publication (Country Code)
uk
Target Audience
一般
Related Material
Applied asymptotics : case studies in small-sample statistics
Data analysis and graphics using R : an example-based approach
Probability : theory and examples
Statistical mechanics of disordered systems : a mathematical perspective
Networks : optimisation and evolution
Regression for categorical data
Brownian motion
Measure theory and filtering : introduction and applications
Probability on trees and networks
Essentials of statistical inference
Random networks for communication : from statistical physics to information systems
Quantum stochastics
Design of Comparative Experiments
Applications of empirical process theory
Wavelet methods for time series analysis
Fundamentals of nonparametric Bayesian inference
Probability : theory and examples
Data analysis and graphics using R : an example-based approach
Numerical methods of statistics
Statistical analysis of stochastic processes in time
Statistical mechanics of disordered systems : a mathematical perspective
Asymptotic statistics
Model selection and model averaging
Random graph dynamics
Predictive statistics : analysis and inference beyond models
Exercises in probability : a guided tour from measure theory to random processes, via conditioning
Random graph dynamics
Essentials of statistical inference
High-dimensional statistics : a non-asymptotic viewpoint
Applications of empirical process theory
Nonparametric estimation under shape constraints : estimators, algorithms, and asymptotics
Statistical principles for the design of experiments
Model-based clustering and classification for data science : with applications in R
Large sample covariance matrices and high-dimensional data analysis
Confidence, likelihood, probability : statistical inference with confidence distributions
Exercises in probability : a guided tour from measure theory to random processes, via conditioning
From finite sample to asymptotic methods in statistics
Random graphs and complex networks
Wavelet methods for time series analysis
Markov chains
Stochastic processes
Measure theory and filtering : introduction and applications
Numerical methods of statistics
Data analysis and graphics using R : an example-based approach
Bootstrap methods and their application
Bayesian methods : an analysis for statisticians and interdisciplinary researchers
Long-range dependence and self-similarity
Semiparametric regression
Bayesian methods : an analysis for statisticians and interdisciplinary researchers
High-dimensional probability : an introduction with applications in data science
Mathematical foundations of infinite-dimensional statistical models
Statistical models
The coordinate-free approach to linear models
Exercises in probability : a guided tour from measure theory to random processes, via conditioning
Statistical models
The estimation and tracking of frequency
A user's guide to measure theoretic probability
Networks : optimisation and evolution
Analysis of multivariate and high-dimensional data
Statistical analysis of stochastic processes in time
Asymptotic statistics
Elements of distribution theory
Saddlepoint approximations with applications
Markov chains
Numerical methods of statistics
Symmetry studies : an introduction to the analysis of structured data in applications
Bayesian nonparametrics
The fundamentals of heavy tails : properties, emergence, and estimation
Probability on trees and networks
Modern discrete probability : an essential toolkit
Mathematical foundations of infinite-dimensional statistical models
Statistical hypothesis testing in context : reproducibility, inference, and science
Random graphs and complex networks
Spectral analysis for univariate time series
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