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Books in Statistics and probability

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1-10 of 233 results in All results

Markov Chains: Theory and Applications

  • 1st Edition
  • Volume 52
  • February 1, 2025
  • C.R. Rao + 1 more
  • English
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 9 5 7 7 - 5
Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters. Each chapter is written by an international board of authors.

Probability Models

  • 1st Edition
  • Volume 51
  • September 20, 2024
  • Arni S.R. Srinivasa Rao + 2 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 3 - 2 9 3 2 8 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 9 3 2 9 - 0
Probability Models, Volume 51 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on Stein’s methods, Probabilities and thermodynamics third law, Random Matrix Theory, General tools for understanding fluctuations of random variables, An approximation scheme to compute the Fisher-Rao distance between multivariate normal distributions, Probability Models Applied to Reliability and Availability Engineering, Backward stochastic differential equation– Stochastic optimization theory and viscous solution of HJB equation, and much more.Additional chapters cover Probability Models in Machine Learning, The recursive stochastic algorithm, randomized urn models and response-adaptive randomization in clinical trials, Random matrix theory: local laws and applications, KOO methods and their high-dimensional consistencies in some multivariate models, Fourteen Lectures on Inference for Stochastic Processes, and A multivariate cumulative damage model and some applications.

Handbook of Statistical Analysis

  • 3rd Edition
  • September 16, 2024
  • Robert Nisbet + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 8 4 5 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 5 8 4 6 - 9
Handbook of Statistical Analysis: AI and ML Applications, third edition, is a comprehensive introduction to all stages of data analysis, data preparation, model building, and model evaluation. This valuable resource is useful to students and professionals across a variety of fields and settings: business analysts, scientists, engineers, and researchers in academia and industry. General descriptions of algorithms together with case studies help readers understand technical and business problems, weigh the strengths and weaknesses of modern data analysis algorithms, and employ the right analytical methods for practical application. This resource is an ideal guide for users who want to address massive and complex datasets with many standard analytical approaches and be able to evaluate analyses and solutions objectively. It includes clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques; offers accessible tutorials; and discusses their application to real-world problems.

An Introduction to Probability and Statistical Inference

  • 3rd Edition
  • May 16, 2024
  • George G. Roussas
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 8 7 2 0 - 9
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 8 7 2 1 - 6
An Introduction to Probability and Statistical Inference, Third Edition guides the reader through probability models and statistical methods to develop critical-thinking skills. Written by award-winning author George Roussas, this valuable text introduces a thinking process to help users obtain the best solution to a posed question or situation and provides a plethora of examples and exercises to illustrate applying statistical methods to different situations.

An Introductory Handbook of Bayesian Thinking

  • 1st Edition
  • April 17, 2024
  • Stephen C. Loftus
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 5 4 5 9 - 4
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 9 1 1 1 - 1
An Introductory Handbook of Bayesian Thinking brings Bayesian thinking and methods to a wide audience beyond the mathematical sciences. Appropriate for students with some background in calculus and introductory statistics, particularly for nonstatisticians with a sufficient mathematical background, the text provides a gentle introduction to Bayesian ideas with a wide array of supporting examples from a variety of fields.

Modeling and Analysis of Longitudinal Data

  • 1st Edition
  • Volume 50
  • February 7, 2024
  • Arni S.R. Srinivasa Rao + 2 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 3 - 1 3 6 5 1 - 1
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 6 5 2 - 8
Longitudinal Data Analysis, Volume 50 in the Handbook of Statistics series covers how data consists of a series of repeated observations of the same subjects over an extended time frame and is thus useful for measuring change. Such studies and the data arise in a variety of fields, such as health sciences, genomic studies, experimental physics, sociology, sports and student enrollment in universities. For example, in health studies, intra-subject correlation of responses must be accounted for, covariates vary with time, and bias can arise if patients drop out of the study.

Probability and Statistics for Physical Sciences

  • 2nd Edition
  • September 5, 2023
  • Brian Martin + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 8 9 6 9 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 8 9 7 0 - 8
Probability and Statistics for Physical Sciences, Second Edition is an accessible guide to commonly used concepts and methods in statistical analysis used in the physical sciences. This brief yet systematic introduction explains the origin of key techniques, providing mathematical background and useful formulas. The text does not assume any background in statistics and is appropriate for a wide-variety of readers, from first-year undergraduate students to working scientists across many disciplines.

Artificial Intelligence

  • 1st Edition
  • Volume 49
  • August 25, 2023
  • Arni S.R. Srinivasa Rao + 2 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 3 - 1 3 7 6 3 - 1
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 7 6 4 - 8
Artificial Intelligence, Volume 49 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics. Chapters in this new release include AI Teacher-Student based Adaptive Structural Deep Learning Model and Its Estimating Uncertainty of Image Data, Machine-derived Intelligence: Computations Beyond the Null Hypothesis, Object oriented basis of artificial intelligence methodologies I in Judicial Systems in India, Artificial Intelligence in Systems Biology, Machine-Learning in Geometry and Physics, Innovation and Machine Learning: Crowdsourcing Open-Source Natural Language Processing (NLP) Algorithms to Advance Public Health Surveillance, and more. Other chapters cover Learning and identity testing of Markov chains, Data privacy for machine learning and statistics, and The interface between AI and Mathematics.

Introduction to Probability Models

  • 13th Edition
  • June 30, 2023
  • Sheldon M. Ross
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 8 7 6 1 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 8 7 6 0 - 5
*Textbook and Academic Authors Association (TAA) McGuffey Longevity Award Winner, 2024*A trusted market leader for four decades, Sheldon Ross’s Introduction to Probability Models offers a comprehensive foundation of this key subject with applications across engineering, computer science, management science, the physical and social sciences and operations research. Through its hallmark exercises and real examples, this valuable course textIntroduction to Probability Models provides the reader with a comprehensive course in the subject, from foundations to advanced topics.

Deep Learning

  • 1st Edition
  • Volume 48
  • February 28, 2023
  • Arni S.R. Srinivasa Rao + 2 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 3 - 1 8 4 3 0 - 7
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 8 4 3 1 - 4
Deep Learning, Volume 48 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Generative Adversarial Networks for Biometric Synthesis, Data Science and Pattern Recognition, Facial Data Analysis, Deep Learning in Electronics, Pattern Recognition, Computer Vision and Image Processing, Mechanical Systems, Crop Technology and Weather, Manipulating Faces for Identity Theft via Morphing and Deepfake, Biomedical Engineering,  and more.

Related subjects

Probability theory and stochastic processes

Statistics