e-books in Probability & Statistics category
by J. C. Lemm - arXiv.org , 2000
A particular Bayesian field theory is defined by combining a likelihood model, providing a probabilistic description of the measurement process, and a prior model, providing the information necessary to generalize from training to non-training data.
by Oscar Sheynin - arXiv.org , 2017
This book covers the history of probability up to Kolmogorov with essential additional coverage of statistics up to Fisher. The book covers an extremely wide field, and is targeted at the same readers as any other book on history of science.
by D. Koutsoyiannis - National Technical University of Athens , 2008
Contents: The utility of probability; Basic concepts of probability; Elementary statistical concepts; Special concepts of probability theory in geophysical applications; Typical univariate statistical analysis in geophysical processes; etc.
by Prasanna Sahoo - University of Louisville , 2013
This book is an introduction to probability and mathematical statistics intended for students already having some elementary mathematical background. It is intended for a one-year junior or senior level undergraduate or beginning graduate course.
by Hossein Pishro-Nik - Kappa Research, LLC , 2014
This book introduces students to probability, statistics, and stochastic processes. It can be used by both students and practitioners in engineering, sciences, finance, and other fields. It provides a clear and intuitive approach to these topics.
by Cosma Rohilla Shalizi - Cambridge University Press , 2013
This is a draft textbook on data analysis methods, intended for a one-semester course for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It began as the lecture notes.
by G. Jay Kerns , 2010
A textbook for an undergraduate course in probability and statistics. The prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.
by Matthias Vallentin , 2012
The cookbook contains a succinct representation of various topics in probability theory and statistics. It provides a comprehensive reference reduced to the mathematical essence, rather than aiming for elaborate explanations.
by O. Melchert - arXiv , 2012
In these lecture notes, a selection of frequently required statistical tools will be introduced and illustrated. They allow to post-process data that stem from, e.g., large-scale numerical simulations (aka sequence of random experiments).
- UCLA , 2012
This book is developed as a free, collaborative and interactive learning environment for elementary probability and statistics education. The book blends information technology, scientific techniques and modern pedagogical concepts.
by Noel Corngold - Caltech , 2009
The book introduces students to the ideas and attitudes that underlie the statistical modeling of physical, chemical, biological systems. The text contains material the author have tried to convey to an audience composed mostly of graduate students.
by Cosma Rohilla Shalizi , 2001
Contents: Probability (Probability Calculus, Random Variables, Discrete and Continuous Distributions); Statistics (Handling of Data, Sampling, Estimation, Hypothesis Testing); Stochastic Processes (Markov Processes, Continuous-Time Processes).
by Brenda Meery - CK-12.org , 2010
CK-12 Foundation's Basic Probability and Statistics– A Short Course is an introduction to theoretical probability and data organization. Students learn about events, conditions, random variables, and graphs and tables that allow them to manage data.
by Marcus Kracht - UCLA , 2005
Contents: Basic Probability Theory (Conditional Probability, Random Variables, Limit Theorems); Elements of Statistics (Estimators, Tests, Distributions, Correlation and Covariance, Linear Regression, Markov Chains); Probabilistic Linguistics.
by Christophe Garban, Jeffrey E. Steif - arXiv , 2011
The goal of this set of lectures is to combine two seemingly unrelated topics: (1) The study of Boolean functions, a field particularly active in computer science; (2) Some models in statistical physics, mostly percolation.
by Terence Tao , 2011
This is a textbook for a graduate course on random matrix theory, inspired by recent developments in the subject. This text focuses on foundational topics in random matrix theory upon which the most recent work has been based.
by Allen B. Downey - Green Tea Press , 2011
Think Stats is an introduction to Probability and Statistics for Python programmers. This new book emphasizes simple techniques you can use to explore real data sets and answer interesting statistical questions. Basic skills in Python are assumed.
by David Blackwell, at al. - IMS , 1996
The bulk of the articles in this volume are research articles in probability, statistics, gambling, game theory, Markov decision processes, set theory and logic, comparison of experiments, games of timing, merging of opinions, etc.
by Marco Taboga - statlect.com , 2010
This e-book is organized as a website that provides access to a series of lectures on fundamentals of probability, statistics and econometrics, as well as to a number of exercises on the same topics. The level is intermediate.
by G. D'Agostini - arXiv , 2010
Triggered by a recent interesting article on the too frequent incorrect use of probabilistic evidence in courts, the author introduces the basic concepts of probabilistic inference with a toy model, and discusses several important issues.
by Cappella Archive - Prasenjit Saha , 2003
This is a short book about the principles of data analysis. The emphasis is on why things are done rather than on exactly how to do them. If you already know something about the subject, then working through this book will deepen your understanding.
by Alexander K. Hartmann - arXiv , 2009
This is a practical introduction to randomness and data analysis, in particular in the context of computer simulations. At the beginning, the most basics concepts of probability are given, in particular discrete and continuous random variables.
by Martin Hairer - arXiv , 2009
This text is an attempt to give a reasonably self-contained presentation of the basic theory of stochastic partial differential equations, taking for granted basic measure theory, functional analysis and probability theory, but nothing else.
by David Aldous, James Allen Fill - University of California, Berkeley , 2014
From the table of contents: General Markov Chains; Reversible Markov Chains; Hitting and Convergence Time, and Flow Rate, Parameters for Reversible Markov Chains; Special Graphs and Trees; Cover Times; Symmetric Graphs and Chains; etc.
by D. A. Levin, Y. Peres, E. L. Wilmer - American Mathematical Society , 2008
An introduction to the modern approach to the theory of Markov chains. The main goal of this approach is to determine the rate of convergence of a Markov chain to the stationary distribution as a function of the size and geometry of the state space.
by Pavel Bleher, Alexander Its - Cambridge University Press , 2001
The book covers broad areas such as topologic and combinatorial aspects of random matrix theory; scaling limits, universalities and phase transitions in matrix models; universalities for random polynomials; and applications to integrable systems.
by Klaus Bichteler - University of Texas , 2002
Written for graduate students of mathematics, physics, electrical engineering, and finance. The students are expected to know the basics of point set topology up to Tychonoff's theorem, general integration theory, and some functional analysis.
by Thomas G. Kurtz - University of Wisconsin , 2007
Covered topics: stochastic integrals with respect to general semimartingales, stochastic differential equations based on these integrals, integration with respect to Poisson measures, stochastic differential equations for general Markov processes.
by G. Larry Bretthorst - Springer , 1988
This work is a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis.
by R. A. Bailey - Cambridge University Press , 2008
This book develops a coherent framework for thinking about factors that affect experiments and their relationships, including the use of Hasse diagrams. The book is ideal for advanced undergraduate and beginning graduate courses.
by David A. Kenny - John Wiley & Sons Inc , 1979
This text is a general introduction to the topic of structural analysis. It presumes no previous acquaintance with causal analysis. It is general because it covers all the standard, as well as a few nonstandard, statistical procedures.
by Wolfgang Härdle - Cambridge University Press , 1992
Nonparametric regression analysis has become central to economic theory. Hardle, by writing the first comprehensive and accessible book on the subject, contributed enormously to making nonparametric regression equally central to econometric practice.
by Albert Tarantola - SIAM , 2004
The first part deals with discrete inverse problems with a finite number of parameters, while the second part deals with general inverse problems. The book for scientists and applied mathematicians facing the interpretation of experimental data.
by Luc Devroye - Springer , 1986
The book on small field on the crossroads of statistics, operations research and computer science. The applications of random number generators are wide and varied. The study of non-uniform random variates is precisely the subject area of the book.
by S.P. Meyn, R.L. Tweedie - Springer , 2005
The book on the theory of general state space Markov chains, and its application to time series analysis, operations research and systems and control theory. An advanced graduate text and a monograph treating the stability of Markov chains.
by Muhammad El-Taha - University of Southern Maine , 2003
Topics: Data Analysis; Probability; Random Variables and Discrete Distributions; Continuous Probability Distributions; Sampling Distributions; Point and Interval Estimation; Large Sample Estimation; Large-Sample Tests of Hypothesis; etc.
by D. Pollard - Springer , 1984
Selected parts of empirical process theory, with applications to mathematical statistics. The book describes the combinatorial ideas needed to prove maximal inequalities for empirical processes indexed by classes of sets or classes of functions.