Results 261 to 270 of about 371,316 (301)
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2007
In this chapter, we introduce the basics of Bayesian data analysis. The key ingredients to a Bayesian analysis are the likelihood function, which reflects information about the parameters contained in the data, and the prior distribution, which quantifies what is known about the parameters before observing data.
Mark E, Glickman, David A, van Dyk
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In this chapter, we introduce the basics of Bayesian data analysis. The key ingredients to a Bayesian analysis are the likelihood function, which reflects information about the parameters contained in the data, and the prior distribution, which quantifies what is known about the parameters before observing data.
Mark E, Glickman, David A, van Dyk
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Variational Bayesian Method for Retinex
IEEE Transactions on Image Processing, 2014In this paper, we propose a variational Bayesian method for Retinex to simulate and interpret how the human visual system perceives color. To construct a hierarchical Bayesian model, we use the Gibbs distributions as prior distributions for the reflectance and the illumination, and the gamma distributions for the model parameters.
Liqian Wang +3 more
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Computational Interaction with Bayesian Methods
Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, 2019This course introduces computational methods in human--computer interaction. Computational interaction methods use computational thinking---abstraction, automation, and analysis---to explain and enhance interaction. This course introduces the theory of practice of computational interaction by teaching Bayesian methods for interaction across four wide ...
Per Ola Kristensson +3 more
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2007
This volume in the Econometric Exercises series contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics.
Koop, G.M., Poirier, D., Tobias, J.
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This volume in the Econometric Exercises series contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics.
Koop, G.M., Poirier, D., Tobias, J.
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A Bayesian Method for Historgrams
Biometrika, 1973SUMMARY This paper describes a Bayesian procedure for the simultaneous estimation of the proba- bilities in a histogram. A two-stage prior distribution is constructed which assumes that probabilities corresponding to adjacent intervals are likely to be closely related.
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Bayesian methods in global optimization
Journal of Global Optimization, 1991The paper reviews methods which have been proposed for solving global optimization problems in the framework of the Bayesian paradigm. Three main approaches are singled out. In the first approach, called the Random Function Approach, methods are based on the idea of introducing a probabilistic model for the objective function in the form of a random ...
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2009
Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information ...
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Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information ...
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Bayesian methods in macroeconometrics
2008Macroeconometrics encompasses a large variety of probability models for macroeconomic time series as well as estimation and inference procedures to study the determinants of economic growth, to examine the sources of business cycle fluctuations, to understand the propagation of shocks, to generate forecasts, and to predict the effects of economic ...
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2002
BACKGROUND AND INTRODUCTION Introduction Motivation and Justification Why Are We Uncertain about Probability? Bayes' Law Conditional Inference with Bayes' Law Historical Comments The Scientific Process in Our Social Sciences Introducing Markov Chain Monte Carlo Techniques Exercises SPECIFYING BAYESIAN MODELS Purpose Likelihood Theory and Estimation The
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BACKGROUND AND INTRODUCTION Introduction Motivation and Justification Why Are We Uncertain about Probability? Bayes' Law Conditional Inference with Bayes' Law Historical Comments The Scientific Process in Our Social Sciences Introducing Markov Chain Monte Carlo Techniques Exercises SPECIFYING BAYESIAN MODELS Purpose Likelihood Theory and Estimation The
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