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Multi-Level Bayesian Models for Environment Perception

methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed;

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Bayesian Analysis of Linear Models

With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date;

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Bayesian Analysis of Linear Models

With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date;

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New Statistics for Design Researchers

of illustrations, tables, simulations and models are presented with full R code and data included. Using Bayesian linear models, multi-level models;

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New Statistics for Design Researchers

of illustrations, tables, simulations and models are presented with full R code and data included. Using Bayesian linear models, multi-level models;

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Visual Analysis of Behaviour

connecting-the-dots for abnormal behaviour detection; discusses Bayesian information criterion, Bayesian networks, bag-of-words representation;

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Bayesian Computation with R

disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms;

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Bayesian Phylogenetics

to obtain answers to very detailed phylogenetic questions. Suitable for graduate-level researchers in statistics and biology, Bayesian;

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Bayesian Methods for Management and Business

and Business: Pragmatic Solutions for Real Problems is an important textbook for Bayesian statistics courses at the advanced MBA-level and;

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Bayesian Hierarchical Models

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian;

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Bayesian Hierarchical Models

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian;

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Computational Bayesian Statistics

a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion;

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Computational Bayesian Statistics

a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion;

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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready;

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Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals

Comprehensive Analysis of Swarm Based Cl is een boek van Harikumar Rajaguru;...

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Practical Applications of Bayesian Reliability

goes on to teach the concepts of design capability and design for reliability; introduce Bayesian models for estimating system reliability;

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Learning Bayesian Models with R: Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world;

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Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems

knowing anything about the learner who uses the system, a system would behave in exactly the same way for all learners. Bayesian Networks for;

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Probabilistic Models of the Brain - Perception and Neural Function

imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed;

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Bayesian Analysis with Python

data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A tutorial for Bayesian;

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Statistical Inference

alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for;

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Statistical Inference

alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for;

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Introduction to Bayesian Estimation and Copula Models of Dependence

, prediction, MCMC,Bayesian regression, and Bayesian analysis of statistical modelsof dependence, and features a focus on copulas for risk management;

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Bayesian Models for Categorical Data

The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology;

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Modeling Multilevel Data in Traffic Safety

, this book proposes a 5 T-level hierarchy, viz. (Geographic region level -- Traffic site level -- Traffic crash level -- Driver-vehicle unit;

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The Theory and Practice of Econometrics

This broadly based graduate--level textbook covers the major models and statistical tools currently used in the practice of econometrics;

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Perception as Bayesian Inference

paradigm for studying human visual perception. This 1996 book provides an introduction to and critical analysis of the Bayesian paradigm. Leading;

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