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Quantum Theory (Dover Books on Physics)
This superb text by David Bohm, formerly Princeton University and Emeritus Professor of Theoretical Physics at Birkbeck College, University of London, provides a formulation of the quantum theory in terms of qualitative and imaginative concepts that have evolved outside and beyond classical theory. Although it presents the main ideas of quantum theory essentially in nonmathematical terms, it follows these with a broad range of specific applications that are worked out in considerable mathematical detail. Addressed primarily to advanced undergraduate students, the text begins with a study of the physical formulation of the quantum theory, from its origin and early development through an analysis of wave vs. particle properties of matter. In Part II, Professor Bohm addresses the mathematical formulation of the quantum theory, examining wave functions, operators, Schrödinger's equation, fluctuations, correlations, and eigenfunctions.Part III takes up applications to simple systems and further extensions of quantum theory formulation, including matrix formulation and spin and angular momentum. Parts IV and V explore the methods of approximate solution of Schrödinger's equation and the theory of scattering. In Part VI, the process of measurement is examined along with the relationship between quantum and classical concepts.Throughout the text, Professor Bohm places strong emphasis on showing how the quantum theory can be developed in a natural way, starting from the previously existing classical theory and going step by step through the experimental facts and theoretical lines of reasoning which led to replacement of the classical theory by the quantum theory.
Author: David Bohm
Published by: Dover Publications | Publication date: 04/25/2012
Kindle book details: Kindle Edition, 673 pages

Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, 2nd Edition
Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features
  • A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ
  • A modern, practical and computational approach to Bayesian statistical modeling
  • A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.
Book DescriptionThe second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.What you will learn
  • Build probabilistic models using the Python library PyMC3
  • Analyze probabilistic models with the help of ArviZ
  • Acquire the skills required to sanity check models and modify them if necessary
  • Understand the advantages and caveats of hierarchical models
  • Find out how different models can be used to answer different data analysis questions
  • Compare models and choose between alternative ones
  • Discover how different models are unified from a probabilistic perspective
  • Think probabilistically and benefit from the flexibility of the Bayesian framework
Who this book is forIf you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected. Table of Contents
  • Thinking probabilistically
  • Programming probabilistically
  • Modeling with Linear Regression
  • Generalizing Linear Models
  • Model Comparison
  • Mixture Models
  • Gaussian Processes
  • Inference Engines
  • Where To Go Next?
  • Published by: Packt Publishing | Publication date: 12/26/2018
    Kindle book details: Kindle Edition, 356 pages

    Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
    A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second Edition
    • Two new chapters on deep belief networks and Gaussian processes
    • Reorganization of the chapters to make a more natural flow of content
    • Revision of the support vector machine material, including a simple implementation for experiments
    • New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
    • Additional discussions of the Kalman and particle filters
    • Improved code, including better use of naming conventions in Python
    Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.
    Published by: Chapman and Hall/CRC | Publication date: 10/08/2014
    Kindle book details: Kindle Edition, 457 pages

    McGraw-Hill's 500 College Precalculus Questions: Ace Your College Exams: 3 Reading Tests + 3 Writing Tests + 3 Mathematics Tests (Mcgraw-hill's 500 Questions)
    Sharpen your skills and prepare for your precalculus exam with a wealth of essential facts in a quick-and-easy Q&A format! Get the question-and-answer practice you need with McGraw-Hill's 500 College Precalculus Questions. Organized for easy reference and intensive practice, the questions cover all essential precalculus topics and include detailed answer explanations.The 500 practice questions are similar to course exam questions so you will know what to expect on test day. Each question includes a fully detailed answer that puts the subject in context. This additional practice helps you build your knowledge, strengthen test-taking skills, and build confidence. From ethical theory to epistemology, this book covers the key topics in precalculus. Prepare for exam day with:
    • 500 essential precalculus questions and answers organized by subject
    • Detailed answers that provide important context for studying
    • Content that follows the current college 101 course curriculum
    Published by: McGraw-Hill Education | Publication date: 12/21/2012
    Kindle book details: Kindle Edition, 176 pages

    An Introduction to Probability and Stochastic Processes (Dover Books on Mathematics)
    Geared toward college seniors and first-year graduate students, this text is designed for a one-semester course in probability and stochastic processes. Topics covered in detail include probability theory, random variables and their functions, stochastic processes, linear system response to stochastic processes, Gaussian and Markov processes, and stochastic differential equations. 1973 edition.
    Published by: Dover Publications | Publication date: 09/18/2013
    Kindle book details: Kindle Edition, 420 pages

    Gaussian Processes on Trees: From Spin Glasses to Branching Brownian Motion (Cambridge Studies in Advanced Mathematics Book 163)
    Branching Brownian motion (BBM) is a classical object in probability theory with deep connections to partial differential equations. This book highlights the connection to classical extreme value theory and to the theory of mean-field spin glasses in statistical mechanics. Starting with a concise review of classical extreme value statistics and a basic introduction to mean-field spin glasses, the author then focuses on branching Brownian motion. Here, the classical results of Bramson on the asymptotics of solutions of the F-KPP equation are reviewed in detail and applied to the recent construction of the extremal process of BBM. The extension of these results to branching Brownian motion with variable speed are then explained. As a self-contained exposition that is accessible to graduate students with some background in probability theory, this book makes a good introduction for anyone interested in accessing this exciting field of mathematics.
    Author: Anton Bovier
    Published by: Cambridge University Press | Publication date: 10/20/2016
    Kindle book details: Kindle Edition, 211 pages

    Physical Biology of the Cell
    Physical Biology of the Cell is a textbook for a first course in physical biology or biophysics for undergraduate or graduate students. It maps the huge and complex landscape of cell and molecular biology from the distinct perspective of physical biology. As a key organizing principle, the proximity of topics is based on the physical concepts that unite a given set of biological phenomena. Herein lies the central premise: that the appropriate application of a few fundamental physical models can serve as the foundation of whole bodies of quantitative biological intuition, useful across a wide range of biological problems. The Second Edition features full-color illustrations throughout, two new chapters, a significantly expanded set of end-of-chapter problems, and is available in a variety of e-book formats.
    Published by: Garland Science | Publication date: 10/29/2012
    Kindle book details: Kindle Edition, 1088 pages

    Einstein Relatively Simple:Our Universe Revealed in Everyday Language
    Einstein Relatively Simple brings together for the first time an exceptionally clear explanation of both special and general relativity. It is for people who always wanted to understand Einstein's ideas but never thought they could.Told with humor, enthusiasm, and rare clarity, this entertaining book reveals how a former high school drop-out revolutionized our understanding of space and time. From E=mc2 and everyday time travel to black holes and the big bang, Einstein Relatively Simple takes us all, regardless of our scientific backgrounds, on a mind-boggling journey through the depths of Einstein's universe. Along the way, we track Einstein through the perils and triumphs of his life — follow his thinking, his logic, and his insights — and chronicle the audacity, imagination, and sheer genius of the man recognized as the greatest scientist of the modern era.In Part I on special relativity we learn how time slows and space shrinks with motion, and how mass and energy are equivalent. Part II on general relativity reveals a cosmos where black holes trap light and stop time, where wormholes form gravitational time machines, where space itself is continually expanding, and where some 13.7 billion years ago our universe was born in the ultimate cosmic event — the Big Bang.Contents:
    • Einstein Discovered: Special Relativity, E = mc2,and Spacetime:
      • From Unknown to Revolutionary
      • The Great Conflict
      • The Two Postulates
      • A New Reality
      • The Shrinking of Time
      • Simultaneity and the Squeezing of Space
      • The World's Most Famous Equation
      • Spacetime
    • Einstein Revealed: General Relativity, Gravity, and the Cosmos:
      • Einstein's Dream
      • “The Happiest Thought of My Life”
      • The Warping of Space and Time
      • Stitching Spacetime
      • What is Spacetime Curvature?
      • Einstein's Masterpiece
      • The Universe Revealed
      • In the Beginning
    Readership: Adults and young people all over the world who are curious about Einstein and how the universe works.
    Published by: WSPC | Publication date: 01/06/2014
    Kindle book details: Kindle Edition, 400 pages

    Elements of Numerical Analysis with Mathematica
    Here we present numerical analysis to advanced undergraduate and master degree level grad students. This is to be done in one semester. The programming language is Mathematica. The mathematical foundation and technique is included. The emphasis is geared toward the two major developing areas of applied mathematics, mathematical finance and mathematical biology.Contents:
    • Beginnings
    • Linear Systems and Optimization
    • Interpolating and Fitting
    • Numerical Differentiation
    • Numerical Integration
    • Numerical Ordinary Differential Equations
    • Monte Carlo Method
    Readership: Undergraduate and master students.Key Features:
    • Develops the topic with Mathematica. Beginning students in numerical analysis need a textbook that uses the programming language they will be using. Many universities use the programming language but few if any textbooks use it
    • Includes much biologically based material, examples and exercises. Mathematical biology is currently the hottest area in Math
    • Includes Monte Carlo method, FDM and numerical ODE methods. Hence suitable for a numerical analysis course in a Financial Math program, another hot topic for mathematics
    • In one semester, it prepares students to take graduate level numerical analysis classes
    Author: John Loustau
    Published by: World Scientific Publishing Company | Publication date: 08/23/2017
    Kindle book details: Kindle Edition, 163 pages

    Multilevel and Longitudinal Modeling Using Stata, Volumes I and II
    Multilevel and Longitudinal Modeling Using Stata, Third Edition, by Sophia Rabe-Hesketh and Anders Skrondal, looks specifically at Stata’s treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are “mixed” because they allow fixed and random effects, and they are “generalized” because they are appropriate for continuous Gaussian responses as well as binary, count, and other types of limited dependent variables.Volume I is devoted to continuous Gaussian linear mixed models and has nine chapters organized into four parts. The first part reviews the methods of linear regression. The second part provides in-depth coverage of two-level models, the simplest extensions of a linear regression model.Volume II is devoted to generalized linear mixed models for binary, categorical, count, and survival outcomes. The second volume has seven chapters also organized into four parts. The first three parts in volume II cover models for categorical responses, including binary, ordinal, and nominal (a new chapter); models for count data; and models for survival data, including discrete-time and continuous-time (a new chapter) survival responses. The fourth and final part in volume II describes models with nested and crossed-random effects with an emphasis on binary outcomes.
    Published by: Stata Press | Publication date: 04/02/2012
    Kindle book details: Kindle Edition, 2 pages
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