Posts Tagged ‘ Bayesian ’

Randomized response, privacy, and Bayes theorem

September 19, 2017
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Randomized response, privacy, and Bayes theorem

Suppose you want to gather data on an incriminating question. For example, maybe a statistics professor would like to know how many students cheated on a test. Being a statistician, the professor has a clever way to find out what he wants to know while giving each student deniability. Randomized response Each student is asked […]

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A Stan case study, sort of: The probability my son will be stung by a bumblebee

August 14, 2017
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A Stan case study, sort of: The probability my son will be stung by a bumblebee

The Stan project for statistical computation has a great collection of curated case studies which anybody can contribute to, maybe even me, I was thinking. But I don’t have time to worry about that right now because I’m on vacation, being on the ...

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On the Origin of "Frequentist" Statistics

July 23, 2017
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Efron and Hastie note that the "frequentist" term "seems to have been suggested by Neyman as a statistical analogue of Richard von Mises' frequentist theory of probability, the connection being made explicit in his 1977 paper, 'Frequentist Probabi...

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Bayes, Jeffreys, MCMC, Statistics, and Econometrics

July 3, 2017
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In Ch. 3 of their brilliant book, Efron and Tibshirani (ET) assert that:Jeffreys’ brand of Bayesianism [i.e., "uninformative" Jeffreys priors] had a dubious reputation among Bayesians in the period 1950-1990, with preference going to subjective analy...

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Effective sample size for MCMC

June 27, 2017
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Effective sample size for MCMC

In applications we’d like to draw independent random samples from complicated probability distributions, often the posterior distribution on parameters in a Bayesian analysis. Most of the time this is impractical. MCMC (Markov Chain Monte Carlo) gives us a way around this impasse. It lets us draw samples from practically any probability distribution. But there’s a […]

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Quick illustration of Metropolis and Metropolis-in-Gibbs Sampling in R

June 4, 2017
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Quick illustration of Metropolis and Metropolis-in-Gibbs Sampling in R

The code below gives a simple implementation of the Metropolis and Metropolis-in-Gibbs sampling algorithms, which are useful for sampling probability densities for which the normalizing constant is difficult to calculate, are irregular, or have high dimension (Metropolis-in-Gibbs). ## Metropolis sampling ## x - current value of Markov chain (numeric vector) ## targ - target log … Continue reading Quick illustration of Metropolis and Metropolis-in-Gibbs Sampling in R →

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Video Introduction to Bayesian Data Analysis, Part 3: How to do Bayes?

May 8, 2017
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Video Introduction to Bayesian Data Analysis, Part 3: How to do Bayes?

This is the last video of a three part introduction to Bayesian data analysis aimed at you who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. If you haven’t watched the other parts yet, I re...

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Freudian hypothesis testing

March 23, 2017
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Freudian hypothesis testing

In his paper Mindless statistics, Gerd Gigerenzer uses a Freudian analogy to describe the mental conflict researchers experience over statistical hypothesis testing. He says that the “statistical ritual” of NHST (null hypothesis significance testing) “is a form of conflict resolution, like compulsive hand washing.” In Gigerenzer’s analogy, the id represents Bayesian analysis. Deep down, a […]

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Video Introduction to Bayesian Data Analysis, Part 2: Why use Bayes?

February 27, 2017
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Video Introduction to Bayesian Data Analysis, Part 2: Why use Bayes?

This is video two of a three part introduction to Bayesian data analysis aimed at you who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. If you haven’t watched part one yet, I really recomme...

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Video Introduction to Bayesian Data Analysis, Part 1: What is Bayes?

February 13, 2017
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Video Introduction to Bayesian Data Analysis, Part 1: What is Bayes?

This is video one of a three part introduction to Bayesian data analysis aimed at you who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. I gave a version of this tutorial at the UseR 2015 conf...

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