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Numerical Methods for Simulation and Optimization of Piecewise Deterministic Markov Processes

, networks and many more. Yet, despite this, there is very little in the way of literature devoted to the development of numerical methods for PDMDs;

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Statistical Inference for Piecewise-deterministic Markov Processes

-deterministic Markov processes offers a detailed and comprehensive survey of state-of-the-art results. It covers a wide range of general processes as;

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Markov Renewal and Piecewise Deterministic Processes

. Generalized Chapman-Kolmogorov equations lead to numerical schemes. The last chapter is an opening on processes for which the deterministic flow;

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Statistical Topics and Stochastic Models for Dependent Data with Applications

, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes;

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Piecewise Deterministic Processes in Biological Models

types of PDMPs appear: continuous time Markov chains, deterministic processes with jumps, processes with switching dynamics, and point processes;

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Basic Concepts in Computational Physics

methods for solving complex mathematical problems and for the direct simulation of physical processes. The book is divided into two main parts;

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Numerical Methods For Stochastic Processes

new methods. Topics presented include Monte Carlo and quasi-Monte Carlo methods, the simulation of major stochastic processes and;

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Mean Field Simulation for Monte Carlo Integration

. Mean Field Simulation for Monte Carlo Integration presents the first comprehensive and modern mathematical treatment of mean field particle;

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Mean Field Simulation for Monte Carlo Integration

. Mean Field Simulation for Monte Carlo Integration presents the first comprehensive and modern mathematical treatment of mean field particle;

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Markov Processes

continuous and jump Markov processes in a way that should appeal especially to physicists and chemists at the senior and graduate level.A;

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Approximate Iterative Algorithms

in functional analysis and probability theory. Extensive applications to Markov decision processes are presented. This volume is intended for;

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Approximate Iterative Algorithms

in functional analysis and probability theory. Extensive applications to Markov decision processes are presented. This volume is intended for;

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Reduced-Order Modeling (ROM) for Simulation and Optimization

order reduction (MOR) for simulation, optimization and control. The topical scope includes, but is not limited to, new out-of-the-box;

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Black Box Simulation Optimization

The term 'simulation optimization' has become widespread in both academical and practical studies. One of the many reasons for this;

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Simulation-Based Optimization

Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted;

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Monte-carlo Methods and Stochastic Processes

focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (PDEs). It covers linear;

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Monte-Carlo Methods and Stochastic Processes

focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (PDEs). It covers linear;

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Controlled Markov Processes and Viscosity Solutions

This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It;

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Controlled Markov Processes and Viscosity Solutions

This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It;

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Numerical Nonsmooth Optimization

, the book is ideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for;

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Numerical Simulation-based Design

This book focuses on numerical simulation-based design theory and methods in mechanical engineering. The simulation-based design;

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Markov Models & Optimization

. This approach is based on the use of a family of Markov processes called Piecewise-Deterministic Processes (PDPs) as a general class;

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Advances in Modeling and Simulation in Textile Engineering

and techniques that can be used to solve textile engineering problems using numerical modeling and simulation. The book draws on innovative;

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Hands-On Simulation Modeling with Python

bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop;

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Industrial and Robotic Systems

, numerical simulations for manipulators and robots, metaheuristics applied to robotics problems, Industry 4.0, control and automation;

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Industrial and Robotic Systems

, numerical simulations for manipulators and robots, metaheuristics applied to robotics problems, Industry 4.0, control and automation;

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Stochastic Optimization Techniques

Federal Armed Forces University Munich, Neubiberg/Munich contains new methods for the approximation and numerical solution of deterministic;

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