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|An Introduction to Mathematical Reasoning
This book eases students into the rigors of university mathematics. The emphasis is on understanding and constructing proofs and writing clear mathematics. The author achieves this by exploring set theory, combinatorics, and number theory, topics that include many fundamental ideas and may not be a part of a young mathematician's toolkit.
O'Reilly� Think Bayes: Bayesian Statistics Made Simple
If you know how to program with Python and also know a little about probability, you�re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you�ll begin to apply these techniques to real-world problems.
An Introduction to Measure Theory
This is a graduate text introducing the fundamentals of measure theory and integration theory, which is the foundation of modern real analysis.
Street-Fighting Mathematics: The Art of Educated Guessing and Opportunistic Problem Solving
This engaging book is an antidote to the rigor mortis brought on by too much mathematical rigor, teaching us how to guess answers without needing a proof or an exact calculation.
Basic Probability Theory
Geared toward advanced undergraduates and graduate students, this introductory text surveys random variables, conditional probability and expectation, characteristic functions, infinite sequences of random variables, Markov chains, and an introduction to statistics. Complete solutions to some of the problems appear at the end of the book.
Applied Stochastic Processes in Science and Engineering
This book introduces modern concepts of applied stochastic processes is written for a broad range of applications in diverse areas of engineering and the sciences. Emphasis is on clarifying the basic principles supporting current prediction techniques.
Applied Statistics has come into existence as an outcome of an experiment and wide experience of more than 40 years. Applied Statistics is intended to introduce the concepts, definitions, and terminology of the subject in an elementary presentation with minimum mathematical background which does not surpass college algebra. Applied Statistics should prepare the reader to make a good decision based on data.
Lectures on Probability Theory and Mathematical Statistics
This book is a collection of lectures on probability theory and mathematical statistics. It provides an accessible introduction to topics that are not usually found in elementary textbooks. It collects results and proofs, especially on probability distributions, that are hard to find in standard references and are scattered here and there in more specialistic books.
Probabilistic Programming and Bayesian Methods for Hackers
This book is designed as an introduction to Bayesian inference from a computational understanding-first, and mathematics-second, point of view. The book assumes no prior knowledge of Bayesian inference nor probabilistic programming.
Probability Theory: The Logic of Science
Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.