Analyzing Data Through Probabilistic Modeling In Statistics
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Analyzing Data Through Probabilistic Modeling In Statistics
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Author : Jakóbczak, Dariusz Jacek
language : en
Publisher: IGI Global
Release Date : 2021-02-19
Analyzing Data Through Probabilistic Modeling In Statistics written by Jakóbczak, Dariusz Jacek and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-19 with Mathematics categories.
Probabilistic modeling represents a subject arising in many branches of mathematics, economics, and computer science. Such modeling connects pure mathematics with applied sciences. Similarly, data analyzing and statistics are situated on the border between pure mathematics and applied sciences. Therefore, when probabilistic modeling meets statistics, it is a very interesting occasion that has gained much research recently. With the increase of these technologies in life and work, it has become somewhat essential in the workplace to have planning, timetabling, scheduling, decision making, optimization, simulation, data analysis, and risk analysis and process modeling. However, there are still many difficulties and challenges that arrive in these sectors during the process of planning or decision making. There continues to be the need for more research on the impact of such probabilistic modeling with other approaches. Analyzing Data Through Probabilistic Modeling in Statistics is an essential reference source that builds on the available literature in the field of probabilistic modeling, statistics, operational research, planning and scheduling, data extrapolation in decision making, probabilistic interpolation and extrapolation in simulation, stochastic processes, and decision analysis. This text will provide the resources necessary for economics and management sciences and for mathematics and computer sciences. This book is ideal for interested technology developers, decision makers, mathematicians, statisticians and practitioners, stakeholders, researchers, academicians, and students looking to further their research exposure to pertinent topics in operations research and probabilistic modeling.
Analyzing Risk Through Probabilistic Modeling In Operations Research
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Author : Jakóbczak, Dariusz Jacek
language : en
Publisher: IGI Global
Release Date : 2015-11-03
Analyzing Risk Through Probabilistic Modeling In Operations Research written by Jakóbczak, Dariusz Jacek and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-03 with Business & Economics categories.
Probabilistic modeling represents a subject spanning many branches of mathematics, economics, and computer science to connect pure mathematics with applied sciences. Operational research also relies on this connection to enable the improvement of business functions and decision making. Analyzing Risk through Probabilistic Modeling in Operations Research is an authoritative reference publication discussing the various challenges in management and decision science. Featuring exhaustive coverage on a range of topics within operational research including, but not limited to, decision analysis, data mining, process modeling, probabilistic interpolation and extrapolation, and optimization methods, this book is an essential reference source for decision makers, academicians, researchers, advanced-level students, technology developers, and government officials interested in the implementation of probabilistic modeling in various business applications.
Handbook Of Probabilistic Models
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Author : Pijush Samui
language : en
Publisher: Butterworth-Heinemann
Release Date : 2019-10-05
Handbook Of Probabilistic Models written by Pijush Samui and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-05 with Computers categories.
Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. Explains the application of advanced probabilistic models encompassing multidisciplinary research Applies probabilistic modeling to emerging areas in engineering Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems
Statistical Analysis Of Stochastic Processes In Time
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Author : J. K. Lindsey
language : en
Publisher: Cambridge University Press
Release Date : 2004-08-02
Statistical Analysis Of Stochastic Processes In Time written by J. K. Lindsey and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-08-02 with Mathematics categories.
This book was first published in 2004. Many observed phenomena, from the changing health of a patient to values on the stock market, are characterised by quantities that vary over time: stochastic processes are designed to study them. This book introduces practical methods of applying stochastic processes to an audience knowledgeable only in basic statistics. It covers almost all aspects of the subject and presents the theory in an easily accessible form that is highlighted by application to many examples. These examples arise from dozens of areas, from sociology through medicine to engineering. Complementing these are exercise sets making the book suited for introductory courses in stochastic processes. Software (available from www.cambridge.org) is provided for the freely available R system for the reader to apply to all the models presented.
Bayesian Analysis With Python
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Author : Osvaldo Martin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-26
Bayesian Analysis With Python written by Osvaldo Martin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-26 with Computers categories.
Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key FeaturesA step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZA modern, practical and computational approach to Bayesian statistical modelingA tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.Book Description The 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 learnBuild 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 modelsFind out how different models can be used to answer different data analysis questionsCompare models and choose between alternative onesDiscover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian frameworkWho this book is for If 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.
Data Analysis And Approximate Models
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Author : Patrick Laurie Davies
language : en
Publisher: CRC Press
Release Date : 2014-07-07
Data Analysis And Approximate Models written by Patrick Laurie Davies and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-07 with Mathematics categories.
The First Detailed Account of Statistical Analysis That Treats Models as Approximations The idea of truth plays a role in both Bayesian and frequentist statistics. The Bayesian concept of coherence is based on the fact that two different models or parameter values cannot both be true. Frequentist statistics is formulated as the problem of estimating the "true but unknown" parameter value that generated the data. Forgoing any concept of truth, Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis presents statistical analysis/inference based on approximate models. Developed by the author, this approach consistently treats models as approximations to data, not to some underlying truth. The author develops a concept of approximation for probability models with applications to: Discrete data Location scale Analysis of variance (ANOVA) Nonparametric regression, image analysis, and densities Time series Model choice The book first highlights problems with concepts such as likelihood and efficiency and covers the definition of approximation and its consequences. A chapter on discrete data then presents the total variation metric as well as the Kullback–Leibler and chi-squared discrepancies as measures of fit. After focusing on outliers, the book discusses the location-scale problem, including approximation intervals, and gives a new treatment of higher-way ANOVA. The next several chapters describe novel procedures of nonparametric regression based on approximation. The final chapter assesses a range of statistical topics, from the likelihood principle to asymptotics and model choice.
Bayesian Analysis With Python
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Author : Osvaldo Martin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31
Bayesian Analysis With Python written by Osvaldo Martin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-31 with Computers categories.
Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries Key Features Conduct Bayesian data analysis with step-by-step guidance Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling Enhance your learning with best practices through sample problems and practice exercises Purchase of the print or Kindle book includes a free PDF eBook. Book DescriptionThe third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection. In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets. By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.What you will learn Build probabilistic models using PyMC and Bambi Analyze and interpret probabilistic models with ArviZ Acquire the skills to sanity-check models and modify them if necessary Build better models with prior and posterior predictive checks Learn the advantages and caveats of hierarchical models Compare models and choose between alternative ones Interpret results and apply your knowledge to real-world problems Explore common models from a unified probabilistic perspective Apply the Bayesian framework's flexibility for probabilistic thinking Who this book is for If you are a student, data scientist, researcher, or 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 scientific libraries like NumPy is expected.
The New Statistical Analysis Of Data
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Author : T.W. Anderson
language : en
Publisher: Springer Science & Business Media
Release Date : 1996-12-13
The New Statistical Analysis Of Data written by T.W. Anderson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-12-13 with Mathematics categories.
A non-calculus based introduction for students studying statistics, business, engineering, health sciences, social sciences, and education. It presents a thorough coverage of statistical techniques and includes numerous examples largely drawn from actual research studies. Little mathematical background is required and explanations of important concepts are based on providing intuition using illustrative figures and numerical examples. The first part shows how statistical methods are used in diverse fields in answering important questions, while part two covers descriptive statistics and considers the organisation and summarisation of data. Parts three to five cover probability, statistical inference, and more advanced statistical techniques.
Discrete Data Analysis With R
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Author : Michael Friendly
language : en
Publisher: CRC Press
Release Date : 2015-12-16
Discrete Data Analysis With R written by Michael Friendly and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-16 with Mathematics categories.
An Applied Treatment of Modern Graphical Methods for Analyzing Categorical DataDiscrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical meth
Foundations Of Linear And Generalized Linear Models
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Author : Alan Agresti
language : en
Publisher: John Wiley & Sons
Release Date : 2015-02-23
Foundations Of Linear And Generalized Linear Models written by Alan Agresti and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-23 with Mathematics categories.
A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.