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Principles of Nonlinear Filtering Theory

by Stephen S.-T. Yau, Xiuqiong Chen, Xiaopei Jiao, Jiayi Kang, Zeju Sun, Yangtianze Tao ...

Series series Mathematics and Statistics (R0)
This text presents a comprehensive and unified treatment of nonlinear filtering theory, with a strong emphasis on its mathematical underpinnings. It is tailored to meet the needs of a diverse readership, including mathematically inclined engineers and scientists at both graduate and post-graduate levels. What sets this book apart from other treatments of the topic is twofold. Firstly, it offers a ... Read more

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This text presents a comprehensive and unified treatment of nonlinear filtering theory, with a strong emphasis on its mathematical underpinnings. It is tailored to meet the needs of a diverse readership, including mathematically inclined engineers and scientists at both graduate and post-graduate levels. What sets this book apart from other treatments of the topic is twofold. Firstly, it offers a
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Festschrift Masatoshi Fukushima: In Honor Of Masatoshi Fukushima's Sanju

by Zhen-qing Chen, Niels Jacob, Masayoshi Takeda, Toshihiro Uemura ...

Series Book 17 - Interdisciplinary Mathematical Sciences
This book contains original research papers by leading experts in the fields of probability theory, stochastic analysis, potential theory and mathematical physics. There is also a historical account on Masatoshi Fukushima's contribution to mathematics, as well as authoritative surveys on the state of the art in the field. ... Read more

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This book contains original research papers by leading experts in the fields of probability theory, stochastic analysis, potential theory and mathematical physics. There is also a historical account on Masatoshi Fukushima's contribution to mathematics, as well as authoritative surveys on the state of the art in the field.
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Teaching and Learning Stochastics

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Edited by Carmen Batanero, Egan J Chernoff ...

Series series ICME-13 Monographs
This book presents a collection of selected papers that represent the current variety of research on the teaching and learning of probability. The respective chapters address a diverse range of theoretical, empirical and practical aspects underpinning the teaching and learning of probability, curricular issues, probabilistic reasoning, misconceptions and biases, as well as their pedagogical ... Read more

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This book presents a collection of selected papers that represent the current variety of research on the teaching and learning of probability. The respective chapters address a diverse range of theoretical, empirical and practical aspects underpinning the teaching and learning of probability, curricular issues, probabilistic reasoning, misconceptions and biases, as well as their pedagogical
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Stochastic Processes and Financial Mathematics

by Ludger Rüschendorf ...

Series series Mathematics and Statistics (R0)
The book provides an introduction to advanced topics in stochastic processes and related stochastic analysis, and combines them with a sound presentation of the fundamentals of financial mathematics. It is wide-ranging in content, while at the same time placing much emphasis on good readability, motivation, and explanation of the issues covered.Financial mathematical topics are first introduced in ... Read more

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The book provides an introduction to advanced topics in stochastic processes and related stochastic analysis, and combines them with a sound presentation of the fundamentals of financial mathematics. It is wide-ranging in content, while at the same time placing much emphasis on good readability, motivation, and explanation of the issues covered.Financial mathematical topics are first introduced in
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Modern Time Series Forecasting with Python

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Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures Free with your book: DRM-free PDF version + access to Packt's next-gen Reader\* Key FeaturesApply ML and global models to improve forecasting accuracy through practical ... Read more

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Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures Free with your book: DRM-free PDF version + access to Packt's next-gen Reader\* Key FeaturesApply ML and global models to improve forecasting accuracy through practical
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Modeling Practical Problems in Complex Systems

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Complex systems—characterized by self-organization, emergence, and profound non-linearity—govern everything from global financial markets to ecological stability. Navigating this complexity requires a unified mathematical framework that moves beyond traditional linear models. This book provides that essential toolkit, offering a robust, interdisciplinary approach to simulation, forecasting, and ... Read more

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Complex systems—characterized by self-organization, emergence, and profound non-linearity—govern everything from global financial markets to ecological stability. Navigating this complexity requires a unified mathematical framework that moves beyond traditional linear models. This book provides that essential toolkit, offering a robust, interdisciplinary approach to simulation, forecasting, and
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Applied Probability and Stochastic Processes

Edited by V. C. Joshua, S. R. S. Varadhan, Vladimir M. Vishnevsky ...

Series series Infosys Science Foundation Series
This book gathers selected papers presented at the International Conference on Advances in Applied Probability and Stochastic Processes, held at CMS College, Kerala, India, on 7–10 January 2019. It showcases high-quality research conducted in the field of applied probability and stochastic processes by focusing on techniques for the modelling and analysis of systems evolving with time. Further, it ... Read more

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This book gathers selected papers presented at the International Conference on Advances in Applied Probability and Stochastic Processes, held at CMS College, Kerala, India, on 7–10 January 2019. It showcases high-quality research conducted in the field of applied probability and stochastic processes by focusing on techniques for the modelling and analysis of systems evolving with time. Further, it
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Earthquake Hazard and Risk

Edited by Vladimír Schenk ...

Series Book 6 - Advances in Natural and Technological Hazards Research
Earthquake Hazard and Risk is a book summarizing selected papers presented at the 27th General Assembly of the International Association of Seismology and Physics of the Earth's Interior (Wellington, January 1994).The papers, rigorously scrutinized by an international board of referees, cover some recent aspects of current research in earthquake hazard and seismic risk. They address the algorithms ... Read more

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Simulation

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The 5th edition of Ross's Simulation continues to introduce aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions ... Read more

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The 5th edition of Ross's Simulation continues to introduce aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions
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Bayesian Methods in the Search for MH370

by Sam Davey, Neil Gordon, Ian Holland, Mark Rutten, Jason Williams ...

Series series SpringerBriefs in Electrical and Computer Engineering
This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was ... Read more

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This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was
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Teoria della Probabilità

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Series series Mathematics and Statistics (R0)
Il libro fornisce un'introduzione concisa ma rigorosa alla Teoria della Probabilità.Fra i possibili approcci alla materia si è scelto quello più moderno, basatosulla teoria della misura: pur richiedendo un grado di astrazione e sofisticazionematematica maggiore, esso è indispensabile a fornire le basi per lo studiodi argomenti più avanzati come i processi stocastici, il calcolo ... Read more

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Fundamentals of Stochastic Models

by Zhe George Zhang ...

Series series Operations Research Series
Stochastic modeling is a set of quantitative techniques for analyzing practical systems with random factors. This area is highly technical and mainly developed by mathematicians. Most existing books are for those with extensive mathematical training; this book minimizes that need and makes the topics easily understandable.Fundamentals of Stochastic Models offers many practical examples and ... Read more

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Asymptotics for Fractional Processes

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Asymptotics for Fractional Processes develops an approach to the large-sample analysis of fractional partial-sum processes, featuring long memory increments. Long memory in a time series, equivalently called strong dependence, is usually defined to mean that the autocovariance sequence is non-summable. The processes studied have a linear moving average representation with a single parameter, ... Read more

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Asymptotics for Fractional Processes develops an approach to the large-sample analysis of fractional partial-sum processes, featuring long memory increments. Long memory in a time series, equivalently called strong dependence, is usually defined to mean that the autocovariance sequence is non-summable. The processes studied have a linear moving average representation with a single parameter,
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Particle emission concept and probabilistic consideration of the development of infections in systems

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The book describes the possibility of making a probabilistic prognosis, which uses the mean n-day logarithm of case numbers in the past to determine an exponent for a probability density for a prognosis, as well as the particle emission concept, which is derived from contact and distribution rates that increase the exponent of the probable development to the extent that a group of people can be
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On Stein's Method for Infinitely Divisible Laws with Finite First Moment

by Benjamin Arras, Christian Houdré ...

Series series SpringerBriefs in Probability and Mathematical Statistics
This book focuses on quantitative approximation results for weak limit theorems when the target limiting law is infinitely divisible with finite first moment. Two methods are presented and developed to obtain such quantitative results. At the root of these methods stands a Stein characterizing identity discussed in the third chapter and obtained thanks to a covariance representation of infinitely ... Read more

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This book focuses on quantitative approximation results for weak limit theorems when the target limiting law is infinitely divisible with finite first moment. Two methods are presented and developed to obtain such quantitative results. At the root of these methods stands a Stein characterizing identity discussed in the third chapter and obtained thanks to a covariance representation of infinitely
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Machine Learning for Finance

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Master ML for financial systems with practical workflows using Python, GenAI, and modern ML deployment tools. Includes hands-on projects in trading, fraud detection, portfolio optimization, and regulatory compliance.Key FeaturesPractical machine learning techniques for financial data and time-series forecastingApply GenAI for synthetic data generation, financial NLP, and reporting automationDeploy ... Read more

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Master ML for financial systems with practical workflows using Python, GenAI, and modern ML deployment tools. Includes hands-on projects in trading, fraud detection, portfolio optimization, and regulatory compliance.Key FeaturesPractical machine learning techniques for financial data and time-series forecastingApply GenAI for synthetic data generation, financial NLP, and reporting automationDeploy
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Semi-Markov Models

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by Yuriy E Obzherin, Elena G Boyko ...

Featuring previously unpublished results, Semi-Markov Models: Control of Restorable Systems with Latent Failures describes valuable methodology which can be used by readers to build mathematical models of a wide class of systems for various applications. In particular, this information can be applied to build models of reliability, queuing systems, and technical control. Beginning with a brief ... Read more

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Featuring previously unpublished results, Semi-Markov Models: Control of Restorable Systems with Latent Failures describes valuable methodology which can be used by readers to build mathematical models of a wide class of systems for various applications. In particular, this information can be applied to build models of reliability, queuing systems, and technical control. Beginning with a brief
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Stochastic Processes

Estimation, Optimisation and Analysis

by Kaddour Najim, Enso Ikonen, Ait-Kadi Daoud ...

A 'stochastic' process is a 'random' or 'conjectural' process, and this book is concerned with applied probability and statistics. Whilst maintaining the mathematical rigour this subject requires, it addresses topics of interest to engineers, such as problems in modelling, control, reliability maintenance, data analysis and engineering involvement with insurance.This book deals with the tools and ... Read more

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A 'stochastic' process is a 'random' or 'conjectural' process, and this book is concerned with applied probability and statistics. Whilst maintaining the mathematical rigour this subject requires, it addresses topics of interest to engineers, such as problems in modelling, control, reliability maintenance, data analysis and engineering involvement with insurance.This book deals with the tools and
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Metrical and Ergodic Theory of Continued Fraction Algorithms

by Gabriela Ileana Sebe, Dan Lascu ...

Series series Mathematics and Statistics (R0)
This monograph presents the work of the authors in metrical theory of continued fractions in the last two decades. The monograph cuts a particular path through this extensive theory and describes the theory in its current form for three families of continued fractions, namely, θ-continued fractions, N-continued fractions, and generalized Rényi continued fractions. The book systematically lays out ... Read more

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This monograph presents the work of the authors in metrical theory of continued fractions in the last two decades. The monograph cuts a particular path through this extensive theory and describes the theory in its current form for three families of continued fractions, namely, θ-continued fractions, N-continued fractions, and generalized Rényi continued fractions. The book systematically lays out
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Regularity Theory and Stochastic Flows for Parabolic ISPDES

by Franco Flandoli ...

The book treats two topics in the theory of stochastic partial differential equations: space-regularity of solutions and existence of stochastic flows. The equations considered in the book are linear parabolic with multiplicative noise, like those arising in non-linear filtering or diffusion models in randomly moving media. Regularity theory in Sobolev spaces is extensively investigated, for ... Read more

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The book treats two topics in the theory of stochastic partial differential equations: space-regularity of solutions and existence of stochastic flows. The equations considered in the book are linear parabolic with multiplicative noise, like those arising in non-linear filtering or diffusion models in randomly moving media. Regularity theory in Sobolev spaces is extensively investigated, for
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Stability Analysis of Regenerative Queueing Models

Mathematical Methods and Applications

by Evsey Morozov, Bart Steyaert ...

The stability analysis of stochastic models for telecommunication systems is an intensively studied topic. The analysis is, as a rule, a difficult problem requiring a refined mathematical technique, especially when one endeavors beyond the framework of Markovian models.The primary purpose of this book is to present, in a unified way, research into the stability analysis of a wide variety of ... Read more

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The stability analysis of stochastic models for telecommunication systems is an intensively studied topic. The analysis is, as a rule, a difficult problem requiring a refined mathematical technique, especially when one endeavors beyond the framework of Markovian models.The primary purpose of this book is to present, in a unified way, research into the stability analysis of a wide variety of
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AI-ML for Decision and Risk Analysis

Challenges and Opportunities for Normative Decision Theory

by Louis Anthony Cox Jr. ...

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This book explains and illustrates recent developments and advances in decision-making and risk analysis. It demonstrates how artificial intelligence (AI) and machine learning (ML) have not only benefitted from classical decision analysis concepts such as expected utility maximization but have also contributed to making normative decision theory more useful by forcing it to confront realistic ... Read more

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This book explains and illustrates recent developments and advances in decision-making and risk analysis. It demonstrates how artificial intelligence (AI) and machine learning (ML) have not only benefitted from classical decision analysis concepts such as expected utility maximization but have also contributed to making normative decision theory more useful by forcing it to confront realistic
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Elements Of Stochastic Dynamics

by Guo-qiang Cai, Weiqiu Zhu ...

Stochastic dynamics has been a subject of interest since the early 20th Century. Since then, much progress has been made in this field of study, and many modern applications for it have been found in fields such as physics, chemistry, biology, ecology, economy, finance, and many branches of engineering including Mechanical, Ocean, Civil, Bio, and Earthquake Engineering.Elements of Stochastic ... Read more

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Stochastic dynamics has been a subject of interest since the early 20th Century. Since then, much progress has been made in this field of study, and many modern applications for it have been found in fields such as physics, chemistry, biology, ecology, economy, finance, and many branches of engineering including Mechanical, Ocean, Civil, Bio, and Earthquake Engineering.Elements of Stochastic
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Modeling and Analysis of Longitudinal Data

by Arni S.R. Srinivasa Rao, Donald E.K. Martin, C.R. Rao ...

Series Book 50 - Handbook of Statistics
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 ... Read more

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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
... Read more

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Recommended For You

Probability eBooks

Principles of Nonlinear Filtering Theory

Festschrift Masatoshi Fukushima: In Honor Of Masatoshi Fukushima's Sanju

Teaching and Learning Stochastics

Stochastic Processes and Financial Mathematics

Modern Time Series Forecasting with Python

Modeling Practical Problems in Complex Systems

Applied Probability and Stochastic Processes

Earthquake Hazard and Risk

Simulation

Bayesian Methods in the Search for MH370

Teoria della Probabilità

Fundamentals of Stochastic Models

Asymptotics for Fractional Processes

Particle emission concept and probabilistic consideration of the development of infections in systems

On Stein's Method for Infinitely Divisible Laws with Finite First Moment

Machine Learning for Finance

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Stochastic Processes

Metrical and Ergodic Theory of Continued Fraction Algorithms

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