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Bayesian Data Analysis for the Behavioral and Neural Sciences

techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general;

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Bayesian Data Analysis for the Behavioral and Neural Sciences

techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general;

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Joint Models of Neural and Behavioral Data

parameters of the cognitive model and allows behavioral data to constrain the neural model. This Bayesian approach can be used to reveal interactions;

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Multivariate Analysis for the Behavioral Sciences, Second Edition

by briefly describing a variety of study designs used in the behavioral sciences, and the concept of models for data analysis. The;

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Multivariate Analysis for the Behavioral Sciences, Second Edition

by briefly describing a variety of study designs used in the behavioral sciences, and the concept of models for data analysis. The;

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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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Bayesian Analysis For Social Sciences

implement the methods using WinBUGS - the most-widely used Bayesian analysis software in the world - and R - an open-source statistical software;

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Data Mining Methods for Knowledge Discovery

of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets;

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Applied Bayesian Modelling

modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics;

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Doing Bayesian Data Analysis

Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian;

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Statistics for the Behavioral Sciences

Statistics for the Behavioral Sciences is a short course and student manual in statistics for the behavioral sciences. It is comprehensive;

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Bayesian Statistics for the Social Sciences

practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference;

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Advanced State Space Methods for Neural and Clinical Data

in neural and clinical data, whether this is characterising time series data from neural spike trains recorded from the rat hippocampus, the primate;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Recent Advances In Stochastic Modeling And Data Analysis

This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover;

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Neural Networks for Applied Sciences and Engineering

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering;

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Market Research and Modeling: Progress and Prospects

Scaling, Conjoint Analysis, Applications of Conjoint and MDS technique, Data Mining, Cluster Analysis, and Neural Networks.;

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Bayesian Networks

Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main;

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Bayesian Statistical Modelling

statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.

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Current Trends in Bayesian Methodology with Applications

latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis;

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Current Trends in Bayesian Methodology with Applications

latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis;

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Bayesian Modeling in Bioinformatics

phylogenic analysis, sparsity priors for protein-protein interaction predictions, and Bayesian networks for gene expression data. The text also;

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