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Random Data

A timely update of the classic book on the theory andapplication of random data analysis First published in 1971, Random Data served as;

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Random Signals

This treatise develops the theory of random processes and its application to the study of systems and the analysis of random data. The text;

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Human Action Analysis with Randomized Trees

trees and the unsupervised random trees. When there are sufficient amount of labeled data available, supervised random trees provides a fast;

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A First Course in Random Matrix Theory

of Big Data and the required computing power to analyse them have rendered classical tools outdated and insufficient. Tools such as random matrix;

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Random Fields for Spatial Data Modeling

This book provides an inter-disciplinary introduction to the theory of random fields and its applications. Spatial models and spatial data;

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Computing and Random Numbers

Sampling Method and Fuzzy -Means and Cluster Ensemble with Random Projection for Big Data Clustering. Provides the reader with insights into the;

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Dynamic Cryptography Technique using Random virtual 2D data tokens

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Processing Random Data

Two features of Processing Random Data differentiate it from other similar books: the focus on computing the reproducibility error for;

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Analyzing Complex Survey Data

Social scientists are demanding more analytic studies of social survey data in order to examine a variety of emerging issues. Answering;

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Data Analysis Using Hierarchical Generalized Linear Models with R

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects;

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Data Analysis Using Hierarchical Generalized Linear Models with R

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects;

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Foundations of Data Science

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high;

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An Introduction to Random Sets

solid initiation into the theory of random sets. It builds the foundation for studying random set data, which, viewed as imprecise or incomplete;

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An Introduction to Random Sets

solid initiation into the theory of random sets. It builds the foundation for studying random set data, which, viewed as imprecise or incomplete;

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Multiscale Analysis of Complex Time Series

of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner. Adopting a data-driven approach;

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An Invitation to Statistics in Wasserstein Space

. Statistics in Wasserstein spaces represents an emerging topic in mathematical statistics, situated at the interface between functional data analysis;

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Random Networks for Communication

When is a random network (almost) connected? How much information can it carry? How can you find a particular destination within the;

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Random Forests with R

and concepts, together with examples and the software tools needed to analyse data using random forests. ;

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Understanding Randomness

-covering a wide variety of conditions,random patterns, and simple models-Understanding Randomness presents a logical sequence of study, through;

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Understanding Randomness

-covering a wide variety of conditions,random patterns, and simple models-Understanding Randomness presents a logical sequence of study, through;

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100 Cases for Medical Data Interpretation

random section of miscellaneous cases. Questions related to each case prompt the reader to consider how the medical data presented might be;

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Exponential Random Graph Models for Social Networks

Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure;

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Exponential Random Graph Models for Social Networks

Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure;

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Components of Variance

effectively by independent, identically distributed random variables or by random sampling from a hypothetical infinite population. Components;

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Components of Variance

effectively by independent, identically distributed random variables or by random sampling from a hypothetical infinite population. Components;

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Statistics 1 (International)

Written to match the contents of the Cambridge syllabus. Statistics 1 corresponds to unit S1. It covers representation of data;

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Random Matrix Theory And Its Applications

Random matrix theory has a long history, beginning in the first instance in multivariate statistics. It was used by Wigner to supply;

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