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Fundamentals of Computational Neuroscience

Third Edition

by Thomas P. Trappenberg ...

Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right. Given the ... Read more

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Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right. Given the
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Modern Time Series Forecasting with Python

Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

by Manu Joseph, Jeffrey Tackes ...

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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Introduction to Probability, Second Edition

by Joseph K. Blitzstein, Jessica Hwang ...

Series series Chapman & Hall/CRC Texts in Statistical Science
Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and toolsfor understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, ... Read more

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Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and toolsfor understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics,
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Examples In Markov Decision Processes

by Alexey B Piunovskiy ...

Series Book 2 - Imperial College Press Optimization Series
This invaluable book provides approximately eighty examples illustrating the theory of controlled discrete-time Markov processes. Except for applications of the theory to real-life problems like stock exchange, queues, gambling, optimal search etc, the main attention is paid to counter-intuitive, unexpected properties of optimization problems. Such examples illustrate the importance of conditions ... Read more

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This invaluable book provides approximately eighty examples illustrating the theory of controlled discrete-time Markov processes. Except for applications of the theory to real-life problems like stock exchange, queues, gambling, optimal search etc, the main attention is paid to counter-intuitive, unexpected properties of optimization problems. Such examples illustrate the importance of conditions
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Pandemics: Insurance and Social Protection

Edited by María del Carmen Boado-Penas, Julia Eisenberg, Şule Şahin‬‬‬ ...

Series series Springer Actuarial
This open access book collects expert contributions on actuarial modelling and related topics, from machine learning to legal aspects, and reflects on possible insurance designs during an epidemic/pandemic.Starting by considering the impulse given by COVID-19 to the insurance industry and to actuarial research, the text covers compartment models, mortality changes during a pandemic, risk-sharing ... Read more

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This open access book collects expert contributions on actuarial modelling and related topics, from machine learning to legal aspects, and reflects on possible insurance designs during an epidemic/pandemic.Starting by considering the impulse given by COVID-19 to the insurance industry and to actuarial research, the text covers compartment models, mortality changes during a pandemic, risk-sharing
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Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases

Edited by Gerardo Chowell, James M. Hyman ...

The contributions by epidemic modeling experts describe how mathematical models and statistical forecasting are created to capture the most important aspects of an emerging epidemic.Readers will discover a broad range of approaches to address questions, such asCan we control Ebola via ring vaccination strategies?How quickly should we detect Ebola cases to ensure epidemic control?What is the ... Read more

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The contributions by epidemic modeling experts describe how mathematical models and statistical forecasting are created to capture the most important aspects of an emerging epidemic.Readers will discover a broad range of approaches to address questions, such asCan we control Ebola via ring vaccination strategies?How quickly should we detect Ebola cases to ensure epidemic control?What is the
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Peeling Random Planar Maps

École d’Été de Probabilités de Saint-Flour XLIX – 2019

by Nicolas Curien ...

Series series Mathematics and Statistics (R0)
These Lecture Notes provide an introduction to the study of those discrete surfaces which are obtained by randomly gluing polygons along their sides in a plane. The focus is on the geometry of such random planar maps (diameter, volume growth, scaling and local limits...) as well as the behavior of statistical mechanics models on them (percolation, simple random walks, self-avoiding random walks... ... Read more

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These Lecture Notes provide an introduction to the study of those discrete surfaces which are obtained by randomly gluing polygons along their sides in a plane. The focus is on the geometry of such random planar maps (diameter, volume growth, scaling and local limits...) as well as the behavior of statistical mechanics models on them (percolation, simple random walks, self-avoiding random walks...
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Matrix-Exponential Distributions in Applied Probability

by Mogens Bladt, Bo Friis Nielsen ...

Series Book 81 - Probability Theory and Stochastic Modelling
This book contains an in-depth treatment of matrix-exponential (ME) distributions and their sub-class of phase-type (PH) distributions. Loosely speaking, an ME distribution is obtained through replacing the intensity parameter in an exponential distribution by a matrix. The ME distributions can also be identified as the class of non-negative distributions with rational Laplace transforms. If the ... Read more

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This book contains an in-depth treatment of matrix-exponential (ME) distributions and their sub-class of phase-type (PH) distributions. Loosely speaking, an ME distribution is obtained through replacing the intensity parameter in an exponential distribution by a matrix. The ME distributions can also be identified as the class of non-negative distributions with rational Laplace transforms. If the
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Large Deviations Applied to Classical and Quantum Field Theory

by Harish Parthasarathy ...

This book deals with a variety of problems in Physics and Engineering where the large deviation principle of probability finds application. Large deviations is a branch of probability theory dealing with approximate computation of the probabilities of rare events.It contains applications of the LDP to pattern recognition problems like analysis of the performance of the EM algorithm for optimal ... Read more

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This book deals with a variety of problems in Physics and Engineering where the large deviation principle of probability finds application. Large deviations is a branch of probability theory dealing with approximate computation of the probabilities of rare events.It contains applications of the LDP to pattern recognition problems like analysis of the performance of the EM algorithm for optimal
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Modelling Non-Markovian Quantum Systems Using Tensor Networks

by Aidan Strathearn ...

Series series Physics and Astronomy (R0)
This thesis presents a revolutionary technique for modelling the dynamics of a quantum system that is strongly coupled to its immediate environment. This is a challenging but timely problem. In particular it is relevant for modelling decoherence in devices such as quantum information processors, and how quantum information moves between spatially separated parts of a quantum system.The key feature ... Read more

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This thesis presents a revolutionary technique for modelling the dynamics of a quantum system that is strongly coupled to its immediate environment. This is a challenging but timely problem. In particular it is relevant for modelling decoherence in devices such as quantum information processors, and how quantum information moves between spatially separated parts of a quantum system.The key feature
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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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Malliavin Calculus in Finance

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by Elisa Alos, David Garcia Lorite ...

Series series Chapman and Hall/CRC Financial Mathematics Series
Malliavin Calculus in Finance: Theory and Practice aims to introduce the study of stochastic volatility (SV) models via Malliavin Calculus.Malliavin calculus has had a profound impact on stochastic analysis. Originally motivated by the study of the existence of smooth densities of certain random variables, it has proved to be a useful tool in many other problems. In particular, it has found ... Read more

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Malliavin Calculus in Finance: Theory and Practice aims to introduce the study of stochastic volatility (SV) models via Malliavin Calculus.Malliavin calculus has had a profound impact on stochastic analysis. Originally motivated by the study of the existence of smooth densities of certain random variables, it has proved to be a useful tool in many other problems. In particular, it has found
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A Forward-Backward SDEs Approach to Pricing in Carbon Markets

by Hinesh Chotai, Mirabelle Muûls, Jean-François Chassagneux ...

Series series Mathematics of Planet Earth
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In Mathematical Finance, the authors consider a mathematical model for the pricing of emissions permits. The model has particular applicability to the European Union Emissions Trading System (EU ETS) but could also be used to consider the modeling of other cap-and-trade schemes. As a response to the risk of Climate Change, carbon markets are currently being implemented in regions worldwide and
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Physik und Finanzen

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Series series Life Science and Basic Disciplines (German Language)
Dieses Buch führt Physikstudenten in die Konzepte und Methoden der Finanzwissenschaft ein. Obwohl die Finanzwissenschaft als recht weit von der Physik entfernt wahrgenommen wird, teilt sie eine Reihe gemeinsamer Methoden und Ideen, die in der Regel mit Rauschen und Unsicherheiten zu tun haben. Durch die Gegenüberstellung der wichtigsten Methoden mit Anwendungen in der Physik und im Finanzwesen ... Read more

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Dieses Buch führt Physikstudenten in die Konzepte und Methoden der Finanzwissenschaft ein. Obwohl die Finanzwissenschaft als recht weit von der Physik entfernt wahrgenommen wird, teilt sie eine Reihe gemeinsamer Methoden und Ideen, die in der Regel mit Rauschen und Unsicherheiten zu tun haben. Durch die Gegenüberstellung der wichtigsten Methoden mit Anwendungen in der Physik und im Finanzwesen
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Stochastic Analysis for Gaussian Random Processes and Fields

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by Vidyadhar S. Mandrekar, Leszek Gawarecki ...

Series series Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic properties connecting probabilistic notions. In particular, it studies Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs).The book begins with preliminary results on covariance and associated RKHS ... Read more

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Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic properties connecting probabilistic notions. In particular, it studies Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs).The book begins with preliminary results on covariance and associated RKHS
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Stochastic Analysis and Applications

by Pinsky ...

Series series Advances in Probability and Related Topics
This volume attempts to exhibit current research in stochastic integration, stochastic differential equations, stochastic optimization and stochastic problems in physics and biology. It includes information on the theory of Dirichlet forms, Feynman integration and the Schrodinger's equation. ... Read more

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This volume attempts to exhibit current research in stochastic integration, stochastic differential equations, stochastic optimization and stochastic problems in physics and biology. It includes information on the theory of Dirichlet forms, Feynman integration and the Schrodinger's equation.
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Stability and Stabilization of Nonlinear Systems with Random Structures

by I. Ya Kats, A.A. Martynyuk ...

Series series Stability and Control: Theory, Methods and Applications
Nonlinear systems with random structures arise quite frequently as mathematical models in diverse disciplines. This monograph presents a systematic treatment of stability theory and the theory of stabilization of nonlinear systems with random structure in terms of new developments in the direct Lyapunov's method. The analysis focuses on dynamic systems with random Markov parameters. This high ... Read more

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Nonlinear systems with random structures arise quite frequently as mathematical models in diverse disciplines. This monograph presents a systematic treatment of stability theory and the theory of stabilization of nonlinear systems with random structure in terms of new developments in the direct Lyapunov's method. The analysis focuses on dynamic systems with random Markov parameters. This high
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Structural Health Monitoring for Suspension Bridges

Interpretation of Field Measurements

by Yang Deng, Aiqun Li ...

This book presents extensive information on structural health monitoring for suspension bridges. During the past two decades, there have been significant advances in the sensing technologies employed in long-span bridge health monitoring. However, interpretation of the massive monitoring data is still lagging behind. This book establishes a series of measurement interpretation frameworks that ... Read more

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This book presents extensive information on structural health monitoring for suspension bridges. During the past two decades, there have been significant advances in the sensing technologies employed in long-span bridge health monitoring. However, interpretation of the massive monitoring data is still lagging behind. This book establishes a series of measurement interpretation frameworks that
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

by Tatiana Tatarenko ...

This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in ... Read more

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This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in
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Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height

by Erik Vanem ...

Series Book 2 - Ocean Engineering & Oceanography
This book provides an example of a thorough statistical treatment of ocean wave data in space and time. It demonstrates how the flexible framework of Bayesian hierarchical space-time models can be applied to oceanographic processes such as significant wave height in order to describe dependence structures and uncertainties in the data.This monograph is a research book and it is partly cross ... Read more

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This book provides an example of a thorough statistical treatment of ocean wave data in space and time. It demonstrates how the flexible framework of Bayesian hierarchical space-time models can be applied to oceanographic processes such as significant wave height in order to describe dependence structures and uncertainties in the data.This monograph is a research book and it is partly cross
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Advances in Statistical Inference for Processes Driven by Fractional Processes

Inference for Fractional Processes

by B L S Prakasa Rao ...

One of the important problems in studying stochastic phenomena is to develop stochastic models and understand their implications behind the phenomenon. Long range dependence is an important stochastic phenomena and it needs study of special type of stochastic processes for modelling. My earlier book on Statistical Inference for Fractional Diffusion Processes (2010) dealt with several aspects for ... Read more

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One of the important problems in studying stochastic phenomena is to develop stochastic models and understand their implications behind the phenomenon. Long range dependence is an important stochastic phenomena and it needs study of special type of stochastic processes for modelling. My earlier book on Statistical Inference for Fractional Diffusion Processes (2010) dealt with several aspects for
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Stochastic Partial Differential Equations with Additive Gaussian Noise

Analysis and Inference

by Ciprian A Tudor ...

The stochastic partial differential equations (SPDEs) arise in many applications of the probability theory. This monograph will focus on two particular (and probably the most known) equations: the stochastic heat equation and the stochastic wave equation.The focus is on the relationship between the solutions to the SPDEs and the fractional Brownian motion (and related processes). An important ... Read more

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The stochastic partial differential equations (SPDEs) arise in many applications of the probability theory. This monograph will focus on two particular (and probably the most known) equations: the stochastic heat equation and the stochastic wave equation.The focus is on the relationship between the solutions to the SPDEs and the fractional Brownian motion (and related processes). An important
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Fundamentals of Stochastic Signals, Systems and Estimation Theory

With Worked Examples

by Branko Kovačević, Željko Đurović, Zoran Banjac ...

Series series Engineering (R0)
Fundamentals of Stochastic Signals, Systems and Estimation Theory (third edition) explains the concepts underlying modeling and analysis of stochastic signals and linear stochastic systems. Two popular stochastic models: the polynomial (or transfer-function) model and the state-space model, are employed in schemes that lead to the successful estimation of unknown signal-/system-model parameters or ... Read more

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Fundamentals of Stochastic Signals, Systems and Estimation Theory (third edition) explains the concepts underlying modeling and analysis of stochastic signals and linear stochastic systems. Two popular stochastic models: the polynomial (or transfer-function) model and the state-space model, are employed in schemes that lead to the successful estimation of unknown signal-/system-model parameters or
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Random Walks and Heat Kernels on Graphs

by Martin T. Barlow ...

Series Book 438 - London Mathematical Society Lecture Note Series
This introduction to random walks on infinite graphs gives particular emphasis to graphs with polynomial volume growth. It offers an overview of analytic methods, starting with the connection between random walks and electrical resistance, and then proceeding to study the use of isoperimetric and Poincaré inequalities. The book presents rough isometries and looks at the properties of a graph that ... Read more

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This introduction to random walks on infinite graphs gives particular emphasis to graphs with polynomial volume growth. It offers an overview of analytic methods, starting with the connection between random walks and electrical resistance, and then proceeding to study the use of isoperimetric and Poincaré inequalities. The book presents rough isometries and looks at the properties of a graph that
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Examples In Markov Decision Processes

Pandemics: Insurance and Social Protection

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Peeling Random Planar Maps

Matrix-Exponential Distributions in Applied Probability

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Modelling Non-Markovian Quantum Systems Using Tensor Networks

Principles of Nonlinear Filtering Theory

Malliavin Calculus in Finance

A Forward-Backward SDEs Approach to Pricing in Carbon Markets

Physik und Finanzen

Stochastic Analysis for Gaussian Random Processes and Fields

Stochastic Analysis and Applications

Stability and Stabilization of Nonlinear Systems with Random Structures

Structural Health Monitoring for Suspension Bridges

Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height

Advances in Statistical Inference for Processes Driven by Fractional Processes

Stochastic Partial Differential Equations with Additive Gaussian Noise

Fundamentals of Stochastic Signals, Systems and Estimation Theory

Random Walks and Heat Kernels on Graphs