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Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias. [PDF]
Cai MB, Schuck NW, Pillow JW, Niv Y.
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ALPHLARD: a Bayesian method for analyzing HLA genes from whole genome sequence data. [PDF]
Hayashi S +6 more
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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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2006
Abstract This chapter provides a brief introduction to the theory and computation of Bayesian statistics and its applications to molecular evolution. It uses simple examples, such as distance estimation under the JC69 model, to introduce the general principles.
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Abstract This chapter provides a brief introduction to the theory and computation of Bayesian statistics and its applications to molecular evolution. It uses simple examples, such as distance estimation under the JC69 model, to introduce the general principles.
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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
openaire +1 more source

