Posts Tagged ‘ Bayesian ’

On the Origin of "Frequentist" Statistics

July 23, 2017
By

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...

Read more »

Bayes, Jeffreys, MCMC, Statistics, and Econometrics

July 3, 2017
By

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...

Read more »

Effective sample size for MCMC

June 27, 2017
By
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 […]

Read more »

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

June 4, 2017
By
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 →

Read more »

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

May 8, 2017
By
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...

Read more »

Freudian hypothesis testing

March 23, 2017
By
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 […]

Read more »

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

February 27, 2017
By
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...

Read more »

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

February 13, 2017
By
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...

Read more »

Beginners Exercise: Bayesian Computation with Stan and Farmer Jöns

January 15, 2017
By
Beginners Exercise: Bayesian Computation with Stan and Farmer Jöns

Over the last two years I’ve occasionally been giving a very basic tutorial to Bayesian statistics using R and Stan. At the end of the tutorial I hand out an exercise for those that want to flex their newly acquired skills. I call this exercise Bay...

Read more »

Subjectivity in statistics

December 15, 2016
By
Subjectivity in statistics

Andrew Gelman on subjectivity in statistics: Bayesian methods are often characterized as “subjective” because the user must choose a prior distribution, that is, a mathematical expression of prior information. The prior distribution requires information and user input, that’s for sure, but I don’t see this as being any more “subjective” than other aspects of a […]

Read more »


Subscribe

Email:

  Subscribe