Tag: Probabilistic

Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text Images


Free Download Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text Images by Alejandro Héctor Toselli , Joan Puigcerver , Enrique Vidal
English | PDF (True) | 2024 | 372 Pages | ISBN : 3031553888 | 13.2 MB
This book provides a comprehensive presentation of a recently introduced framework, named "probabilistic indexing" (PrIx), for searching text in large collections of document images and other related applications. It fosters the development of new search engines for effective information retrieval from manuscripts which, however, lack the electronic text (transcripts) that would typically be required for such search and retrieval tasks.

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Probabilistic Machine Learning for Finance and Investing (Early Release)


Free Download Probabilistic Machine Learning for Finance and Investing (Early Release) by Deepak Kanungo
English | 2022 | ISBN: 9781492097662 | 88 pages | PDF | 2.55 Mb
Whether based on academic theories or machine learning strategies, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability theory.

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POPULATION DYNAMICS ALGEBRAIC AND PROBABILISTIC APPROACH


Free Download POPULATION DYNAMICS: ALGEBRAIC AND PROBABILISTIC APPROACH by Utkir a Rozikov
English | May 6, 2020 | ISBN: 9811211221 | 460 pages | MOBI | 67 Mb
A population is a summation of all the organisms of the same group or species, which live in a particular geographical area, and have the capability of interbreeding. The main mathematical problem for a given population is to carefully examine the evolution (time dependent dynamics) of the population. The mathematical methods used in the study of this problem are based on probability theory, stochastic processes, dynamical systems, nonlinear differential and difference equations, and (non-)associative algebras.

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Probabilistic Conditional Independence Structures


Free Download Probabilistic Conditional Independence Structures by Milan Studený
English | PDF | 2005 | 292 Pages | ISBN : 1852338911 | 2.4 MB
Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods of their description, and takes an algebraic approach.

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Probabilistic Machine Learning for Finance and Investing (Sixth Early Release)


Free Download Probabilistic Machine Learning for Finance and Investing
English | 2023 | ISBN: 9781492097662 | 217 Pages | MOBI EPUB (True) | 22 MB
Unlike conventional AI systems, probabilistic machine learning (ML) systems treat errors and uncertainties as features, not bugs. They quantify uncertainty generated from inexact model inputs and outputs as probability distributions, not point estimates. Most importantly, these systems are capable of forewarning us when their inferences and predictions are no longer useful in the current market environment. These ML systems provide realistic support for financial decision-making and risk management in the face of uncertainty and incomplete information.

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Probabilistic and Causal Inference The Works of Judea Pearl


Free Download Hector Geffner, Rita Dechter, Joseph Y. Halpern, "Probabilistic and Causal Inference: The Works of Judea Pearl"
English | 2022 | ISBN: 1450395864, 1450395872 | PDF | pages: 946 | 20.0 mb
Professor Judea Pearl won the 2011 Turing Award "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning." This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988-2001), and causality, recent period (2002-2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl’s work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.

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The Probabilistic SIR Model (PSIR) in the Pandemic Process Project Management in Prevention and Support


Free Download The Probabilistic SIR Model (PSIR) in the Pandemic Process: Project Management in Prevention and Support
English | 2023 | ISBN: 3031311892 | 89 Pages | PDF EPUB (True) | 22 MB
With all the insights experienced in the COVID process, one essential remains: "The virus remains a constant companion". In contrast to regularly occurring infection processes, a COVID infection takes a different course. This is characterized by a dynamic that deviates from conventional, well-known processes in that the originators change their identity and develop corresponding variants. Therefore, preventive infection management – supported by statistical-probabilistic analyzes with PSIR – is important for preventive management of resources and infrastructure for the "waves ahead of the wave".

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Probabilistic Topic Models Foundation and Application


Free Download Probabilistic Topic Models: Foundation and Application
English | 2023 | ISBN: 9819924308 | 227 Pages | PDF EPUB (True) | 13 MB
This book introduces readers to the theoretical foundation and application of topic models. It provides readers with efficient means to learn about the technical principles underlying topic models. More concretely, it covers topics such as fundamental concepts, topic model structures, approximate inference algorithms, and a range of methods used to create high-quality topic models. In addition, this book illustrates the applications of topic models applied in real-world scenarios. Readers will be instructed on the means to select and apply suitable models for specific real-world tasks, providing this book with greater use for the industry. Finally, the book presents a catalog of the most important topic models from the literature over the past decades, which can be referenced and indexed by researchers and engineers in related fields. We hope this book can bridge the gap between academic research and industrial application and help topic models play an increasingly effective role in both academia and industry.

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