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Lecture Notes for Introductory Probability
The contents include: Combinatorics, Axioms of Probability, Conditional Probability and Independence, Discrete Random Variables, Continuous Random Variables, Joint Distributions and Independence, More on Expectation and Limit Theorems, Convergence in probability, Moment generating functions, Computing probabilities and expectations by conditioning, Markov Chains: Introduction, Markov Chains: Classification of States, Branching processes, Markov Chains: Limiting Probabilities, Markov Chains: Reversibility, Three Application, Poisson Process.
Author(s): Janko Gravner, Mathematics Department, University of California
218 Pages
Probability Theory and Statistics Lecture notes
The aim of the notes is to combine the mathematical and theoretical underpinning of statistics and statistical data analysis with computational methodology and practical applications. Topics covered includes: Notion of probabilities, Probability Theory, Statistical models and inference, Mean and Variance, Sets, Combinatorics, Limits and infinite sums, Integration.
Author(s): Niels Richard Hansen
294 Pages
Notes on Probability Theory and Statistics
This note explains the following topics: Probability Theory, Random Variables, Distribution Functions, And Densities, Expectations And Moments Of Random Variables, Parametric Univariate Distributions, Sampling Theory, Point And Interval Estimation, Hypothesis Testing, Statistical Inference, Asymptotic Theory, Likelihood Function, Neyman or Ratio of the Likelihoods Tests.
Author(s): Antonis Demos
138 Pages
Lecture Notes Probability Theory
This book explains the following topics: Probability spaces, Random variables, Independence, Expectation, Convergence of sequences of random variables.
Author(s): Manuel Cabral Morais
275 Pages
Markov Random Fields and Their Applications
This book presents the basic ideas of the subject and its application to a wider audience. Topics covered includes: The Ising model, Markov fields on graphs, Finite lattices, Dynamic models, The tree model and Additional applications.
Author(s): Ross Kindermann and J. Laurie Snell
147 Pages
Lecture Notes on Probability Theory and Random Processes
The goal to to help the student figure out the meaning of various concepts in Probability Theory and to illustrate them with examples. Topics covered includes: Modelling Uncertainty, Probability Space, Conditional Probability and Independence, Random Variable, Conditional Expectation, Gaussian Random Variables, Limits of Random Variables, Filtering Noise and Markov Chains
Author(s): Jean Walrand
302 Pages
Probability, Random Processes, and Ergodic Properties
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Grinstead and Snells Introduction to Probability
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MEASURE INTEGRATION PROBABILITY
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Introduction to probability and random processes
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Introduction to Probability pdf
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Lectures on Stochastic Analysis
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Introduction to Statistical Signal Processing Gray R.M. and Davisson L.D
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Lecture Notes on Probability Theory and Random Processes (Walrand J pdf)
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Probability Lecture Notes (Santos D pdf)
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