Probability & Statistics

'Lucid, calm, informed, directly helpful in trying to think about where we are now... The literature of the time after begins here' Evening Standard'Taking a breather from bewildering statistics and terrible tales of contagion to read Giordano's book was...

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion,...

A comprehensive and up-to-date introduction to the mathematics that all economics students need to knowProbability theory is the quantitative language used to handle uncertainty and is the foundation of modern statistics. Probability and Statistics for...

Containing many results that are new, or which exist only in recent research articles, this thoroughly revised third edition of Interest Rate Modeling: Theory and Practice, Third Edition portrays the theory of interest rate modeling as a...

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed...

Based on a lecture course given at Chalmers University of Technology, this 2002 book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before...

Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing an appropriate...

This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits, they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to...

Written in an accessible and clear manner, Straightforward Statistics with Excel (R) 2e by Chieh-Chen Bowen helps students across the social and behavioral sciences gradually build their skills to develop a better understanding of the world around them...

Mathematics is beautiful--and it can be fun and exciting as well as practical. Good Math is your guide to some of the most intriguing topics from two thousand years of mathematics: from Egyptian fractions to Turing machines; from the real meaning of...

What are the odds against winning the Lottery, making money in a casino, or backing the right horse? Every day, people make judgements on these matters and face other decisions that rest on their understanding of probability: buying insurance, following...

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction...

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a...

This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are...

From crime figures to health scares, election polls to stock market forecasts, numbers make the news all the time. But are they accurate? John Allen Paulos, travels through the pages of an average newspaper, revealing how mathematics is at the heart of...

This book offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, as well as basics of sampling distributions, estimation and hypothesis testing. As a companion for classes for engineers and...

This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on...

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected...

This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most...

Students in the sciences, economics, social sciences, and medicine take an introductory statistics course. And yet statistics can be notoriously difficult for instructors to teach and for students to learn. To help overcome these challenges, Gelman and...

THE GUIDE FOR ANYONE AFRAID TO LEARN STATISTICS & ANALYTICS UPDATED WITH NEW EXAMPLES & EXERCISES This book discusses statistics and analytics using plain language and avoiding mathematical jargon. If you thought you couldn't learn these data analysis...

This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data.The book has been updated...

The aims of this book, originally published in 1982, are to give an understanding of the basic ideas concerning stochastic differential equations on manifolds and their solution flows, to examine the properties of Brownian motion on Riemannian manifolds...

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about...

This book provides a user-friendly and practical guide on R, with emphasis on covering a broader range of statistical methods than previous books on R. This is a "how to" book and will be of use to undergraduates and graduate students along...

High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to...

Students and instructors alike will benefit from this rigorous, unfussy text, which keeps a clear focus on the basic probabilistic concepts required for an understanding of financial market models, including independence and conditioning. Assuming only...

Every business area relies on an understanding of statistics to succeed. Statistics for Business and Economics by Carlos Cortinhas and Ken Black shows students that the proper application of statistics in the business world goes hand-in-hand with good...

Written by two leading experts on experimental methods, this concise text covers the major aspects of experiment design, analysis, and interpretation in clear language. Students learn how to design randomized experiments, analyze the data, and interpret...

Introductory logic is generally taught as a straightforward technical discipline. In this book, John MacFarlane helps the reader think about the limitations of, presuppositions of, and alternatives to classical first-order predicate logic, making this an...

Building on the Handbook of Regression Analysis and Regression Analysis by Example, the authors' thorough treatments of "classic" regression analysis, this book covers two important and more advanced topics of time-to-event survival data and...

In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health...

What is Big Data, and why should you care? Big data knows where you've been and who your friends are. It knows what you like and what makes you angry. It can predict what you'll buy, where you'll be the victim of crime and when you'll have a heart...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781801053495Author Clare MichellePage Count 10Imprint Imagine That Publishing LtdPublisher Imagine That Publishing LtdDimensions(mm) 210mm * 176mm * 15mm

Have you been told you need to do multilevel modeling, but you can't get past the forest of equations? Do you need the techniques explained with words and practical examples so they make sense? Help is here! This book unpacks these statistical...

This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing...

Regression Analysis in R: A Comprehensive View for the Social Sciences covers the basic applications of multiple linear regression all the way through to more complex regression applications and extensions. Written for graduate level students of social...

This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the...

The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort...

The fourth edition of this widely used textbook on pricing and hedging of financial derivatives now also includes dynamic equilibrium theory and continues to combine sound mathematical principles with economic applications. Concentrating on the...