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Stochastic Computing

This book covers the history and recent developments of stochastic computing. Stochastic computing (SC) was first introduced in the 1960s;

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Quantum Communication, Computing, and Measurement 2

, quantum computing, stochastic processes and filtering, and quantum measurement theory ;

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Stochastic Biomathematical Models

Stochastic biomathematical models are becoming increasingly important as new light is shed on the role of noise in living systems;

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Advanced Computing in Industrial Mathematics

focus on: mathematical physics, numerical analysis, high performance computing, optimization and control, mathematical biology, stochastic;

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Recent Advances in Scientific Computing and Applications

-optics, biofilms, finance, magnetohydrodynamics flow, electromagnetic waves, the fluid-structure interaction problem, and stochastic PDEs;

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Advances in Machine Learning and Computational Intelligence

; fuzzy, stochastic and probabilistic computing; neural computing; big data; social networks and applications of soft computing in various areas.;

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Stochastic Programming

This book is concerned with fostering theoretical issues on stochastic programming and discussing how it can solve real life problems. The;

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Stochastic Integration with Jumps

Stochastic processes with jumps and random measures are importance as drivers in applications like financial mathematics and signal;

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Stochastic Integration with Jumps

Stochastic processes with jumps and random measures are importance as drivers in applications like financial mathematics and signal;

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Stochastic Simulation Optimization

With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering;

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Stochastic Geometry for Wireless Networks

Covering point process theory, random geometric graphs and coverage processes, this rigorous introduction to stochastic geometry will;

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Recent Advances In Stochastic Modeling And Data Analysis

This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover;

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Complex Stochastic Systems

Complex stochastic systems comprises a vast area of research, from modelling specific applications to model fitting, estimation procedures;

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Complex Stochastic Systems

Complex stochastic systems comprises a vast area of research, from modelling specific applications to model fitting, estimation procedures;

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Stochastic Simulation Optimization For Discrete Event Systems

millions or even billions of events with many variables and constraints, modeling these stochastic simulations has long been a hard nut to crack;

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Horizons in Computer Science Research

discrete-time stochastic systems; and virtual supercomputers as the basis of scientific computing.;

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Modern Methods in Scientific Computing and Applications

. The remainder of the book is concerned with combining techniques with concrete applications: stochastic differential equations, image;

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Algebraic Informatics

topics such as data models and coding theory; fundamental aspects of cryptography and security; algebraic and stochastic models of computing;

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Uncertainty Quantification in Computational Fluid Dynamics

stochastic Galerkin methods and modified versions of the stochastic collocation methods that use adaptive stencil selection of the ENO-WENO type;

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Introduction to Stochastic Calculus Applied to Finance

more sophisticated mathematical concepts, such as stochastic integration, to describe the behavior of markets and to derive computing methods;

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Introduction to Stochastic Calculus Applied to Finance

more sophisticated mathematical concepts, such as stochastic integration, to describe the behavior of markets and to derive computing methods;

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Recent Trends in Mathematical Modeling and High Performance Computing

control High performance and scientific computing Stochastic models and statistics Recent Trends in Mathematical;

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American-Type Options

The book gives a systematical presentation of stochastic approximation methods for discrete time Markov price processes. Advanced methods;

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Quantum Fields and Processes

theory and, by extension, in the Hudson-Parthasarathy theory of quantum stochastic processes, quantum mechanics, stochastic processes, and;

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Applied Stochastic Modelling

Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience;

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Applied Stochastic Modelling

Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience;

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Introduction To Computational Mathematics

algorithms, numerical integration, numerical methods of partial differential equations, finite element methods, optimization algorithms, stochastic;

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