Posts Tagged ‘ Bayesian ’

The Problem With Bayesian Model Averaging…

December 3, 2017
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The problem is that one of the models considered is traditionally assumed true (explicitly or implicitly) since the prior model probabilities sum to one. Hence all posterior weight gets placed on a single model asymptotically -- just what you don't wa...

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Modeling With Mixed-Frequency Data

November 26, 2017
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Here's a bit more related to the FRB St. Louis conference.The fully-correct approach to mixed-frequency time-series modeling is: (1) write out the state-space system at the highest available data frequency or higher (e.g., even if your highest frequenc...

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Modeling With Mixed-Frequency Data

November 26, 2017
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Here's a bit more related to the FRB St. Louis conference.The fully-correct approach to mixed-frequency time-series modeling is: (1) write out the state-space system at the highest available data frequency or higher (e.g., even if your highest frequenc...

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Quantifying information gain in beta-binomial Bayesian model

November 13, 2017
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The beta-binomial model is the “hello world” example of Bayesian statistics. I would call it a toy model, except it is actually useful. It’s not nearly as complicated as most models used in application, but it illustrates the basics of Bayesian inference. Because it’s a conjugate model, the calculations work out trivially. For more on […]

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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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Bayesian methods at Bletchley Park

July 25, 2017
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Bayesian methods at Bletchley Park

From Nick Patterson’s interview on Talking Machines: GCHQ in the ’70s, we thought of ourselves as completely Bayesian statisticians. All our data analysis was completely Bayesian, and that was a direct inheritance from Alan Turing. I’m not sure this has ever really been published, but Turing, almost as a sideline during his cryptoanalytic work, reinvented […]

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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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