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Probabilistic conjunctive models

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Handbook of Probabilistic Models

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields;

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Practical Probabilistic Programming

the future! Probabilistic programming is a programming paradigm in which code models are used to draw probabilistic inferences from data. By;

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Probabilistic Models of the Brain - Perception and Neural Function

A survey of probabilistic approaches to modeling and understanding brain function. Neurophysiological, neuroanatomical, and brain;

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Probabilistic Models in Building Automation

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Biological Sequence Analysis

Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing;

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Mastering Probabilistic Graphical Models Using Python

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Random Signals

covers three important areas: fundamentals and examples of random process models; application of probabilistic models - signal detection and;

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Probabilistic Models of the Brain

address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and;

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Non-Gaussian Autoregressive-Type Time Series

most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time;

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Bayesian Analysis with Python

-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian;

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Probabilistic Graphical Models

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available;

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Stochastic Models of Uncertainties in Computational Mechanics

support robust optimization, design, and updating. A probabilistic approach to uncertainties is the most powerful, efficient, and effective;

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Query Processing on Probabilistic Data

Probabilistic data is motivated by the need to model uncertainty in large databases. Over the last twenty years or so, both the Database;

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Probabilistic Graphical Models

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical;

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Probabilistic Graphical Models

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical;

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Probabilistic Transmission System Planning

, which discuss the concepts, models, methods and data in probabilistic transmission planning. The second part, Chapters 8 to 11, addresses four;

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Probabilistic Deep Learning

Probabilistic Deep Learning shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty;

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Probabilistic Reasoning in Multiagent Systems

probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an;

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Probabilistic Reasoning in Multiagent Systems

probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an;

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Learning Probabilistic Graphical Models in R

Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R About This Book;

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Common Waters, Diverging Streams

This book is a firsthand investigation into water management in a fast-growing region of the arid Am;...

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Probabilistic models for computer networks

Probabilistic models for computer networks: Tools and solved problems overviews the main probabilistic tools and theory used in the;

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An Introduction to Stochastic Orders

An Introduction to Stochastic Orders discusses this powerful tool that can be used in comparing probabilistic models in different;

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Probabilistic Safety Assessment for Optimum Nuclear Power Plant Life Management (PLiM)

Probabilistic safety assessment methods are used to calculate nuclear power plant durability and resource lifetime. Successful calculation;

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Information Retrieval Models

scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best;

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Bayesian Networks and Decision Graphs

This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical;

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