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On the Influence of Non-Perfect Randomness on Probabilistic Algorithms

results are presented that describe implications of using non-perfect random numbers with probabilistic algorithms. In the first part several;

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A First Course in Random Matrix Theory

algorithms. Presenting an introductory calculus course for random matrices, the book focusses on modern concepts in matrix theory, generalising the;

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Perfect Simulation

from interacting distributions. Perfect Simulation illustrates the application of perfect simulation ideas and algorithms to a wide;

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Data Structures and Algorithms Using C#

is the perfect resource for C# professionals and students alike.;

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Data Structures and Algorithms Using C#

is the perfect resource for C# professionals and students alike.;

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Algorithms for Optimization

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a;

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Algorithmic Learning in a Random World

of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The;

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Algorithmic Learning in a Random World

of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The;

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Probabilistic Robotics

in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic;

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Average Case Analysis of Algorithms on Sequences

A timely book on a topic that has witnessed a surge of interest over the last decade, owing in part to several novel applications, most;

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Business Forecasting Deal

in pursuit of the perfect forecast * Introduces a new way to think about business forecasting, focusing on process efficiency and the elimination;

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Data-Driven Computational Neuroscience

book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification;

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Analysis of Algorithms

McConnell (Canisius College) developed this course to make students aware of the impact of algorithms on a program's efficiency and;

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Combinatorial Algorithms

This book constitutes the thoroughly refereed post-workshop proceedings for the 26 International Workshop on combinatorial Algorithms;

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Fundamentals of Algorithmics

introduction to the analysis of algorithms, and uses coherent notation and solving recurrences. It also includes a chapter on probabilistic algorithms;

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Fundamentals Of Algorithmics

introduction to the analysis of algorithms, and uses coherent notation and solving recurrences. It also includes a chapter on probabilistic algorithms;

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Multivariate Algorithms and Information-Based Complexity

The contributions by leading experts in this book focus on a variety of topics of current interest related to information-based complexity;

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Information Retrieval Models

probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link;

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Design and Analysis of Algorithms

algorithms, sorting algorithm, backtracking algorithms and search algorithms. The text also focuses on heuristics, dynamic programming and meta;

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Genetics and Randomness

of all essential processes, effectively embracing a probabilistic understanding of the phenomena of life.;

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Bayesian Programming

probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism;

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Bayesian Programming

probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism;

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Randomized Algorithms

probabilistic analysis that are recurrent in algorithmic applications. Algorithmic examples are given to illustrate the use of each tool in a concrete;

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Probabilistic Extensions of Various Logical Systems

The contributions in this book survey results on combinations of probabilistic and various other classical, temporal and justification;

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Planning with markov Decision Processes

reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent;

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Randomness

test, in the reliability of DNA findings as legal evidence, or in the likelihood of passing on a deadly congenital disease--yet as few people;

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Randomness

test, in the reliability of DNA findings as legal evidence, or in the likelihood of passing on a deadly congenital disease--yet as few people;

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