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Probabilistic Models of the Brain - Perception and Neural Function

. This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples;

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Probabilistic Models of the Brain

Bayesian inference to derive models of brain function. This book surveys some of the current probabilistic approaches to modeling and understanding;

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Data-Driven Computational Neuroscience

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain;

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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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Quantum Models of Cognition and Decision

traditional models. They introduce the foundations for modeling probabilistic-dynamic systems using two aspects of quantum theory. The first;

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Quantum Models of Cognition and Decision

traditional models. They introduce the foundations for modeling probabilistic-dynamic systems using two aspects of quantum theory. The first;

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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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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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Random Signals

filtering; and statistical estimation - the measurement and analysis of random data to determine the structure and parameter values of probabilistic;

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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

. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model;

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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

to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an;

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Probabilistic Graphical Models

to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an;

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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

This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed;

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Probabilistic Reasoning in Multiagent Systems

This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed;

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Learning Probabilistic Graphical Models in R

* Transform the old linear regression model into a powerful probabilistic model * Use standard industry models but with the power of PGM;

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

of the reliability and ageing of components is critical for forecasting safety and directing preventative maintenance, and Probabilistic safety;

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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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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

stochastic orders, in the univariate and multivariate cases, along with applications to probabilistic models. Introduces stochastic;

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Active Inference

The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple;

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Vital Models

The use of models has been important to the historical and contemporary study of the human brain, yet very little study by social;

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