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Biological Sequence Analysis

probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from;

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Visual Analysis of Behaviour

, modelling rare behaviours, and man-in-the-loop active learning; examines multi-camera behaviour correlation, person re-identification, and;

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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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Handbook of Probabilistic Models

that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil;

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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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Practical Probabilistic Programming

trends, computer system failures, experimental outcomes, and other critical concerns. Practical Probabilistic Programming explains;

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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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Data-Driven Computational Neuroscience

trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic;

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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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Random Signals

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

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Information Retrieval Models

randomness (DFR). Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: It is easy;

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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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Bayesian and grAphical Models for Biomedical Imaging

submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops;

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Probabilistic conjunctive models

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Understanding Probability Models

of the probabilistic aspects of two Metaheuristics: Simulated Annealing and Tabu Search. How to obtain the identification of econometric;

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Hardware-Aware Probabilistic Machine Learning Models

augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed;

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Query Processing on Probabilistic Data

community and the Al community have studied various aspects of probabilistic relational data. Query Processing on Probabilistic Data: A Survey;

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Probabilistic Graphical Models

with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides;

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Probabilistic Graphical Models

with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides;

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Probabilistic Reasoning in Multiagent Systems

in multiple cooperative agents, and effective, exact and distributed probabilistic inference.;

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Probabilistic Reasoning in Multiagent Systems

in multiple cooperative agents, and effective, exact and distributed probabilistic inference.;

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Probabilistic Models in Building Automation

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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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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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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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