Posts Tagged ‘ Bayesian ’

Monadic probabilistic programming in Scala with Rainier

June 1, 2018
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Monadic probabilistic programming in Scala with Rainier

Introduction Rainier is an interesting new probabilistic programming library for Scala recently open-sourced by Stripe. Probabilistic programming languages provide a computational framework for building and fitting Bayesian models to data. There are many interesting probabilistic programming languages, and there is currently a lot of interesting innovation happening with probabilistic programming languages embedded in strongly typed … Continue reading Monadic probabilistic programming in Scala with Rainier

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Six sigma events

May 31, 2018
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Six sigma events

I saw on Twitter this afternoon a paraphrase of a quote from Nassim Taleb to the effect that if you see a six-sigma event, that’s evidence that it wasn’t really a six-sigma event. What does that mean? Six sigma means six standard deviations away from the mean of a probability distribution, sigma (σ) being the […]

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Hypothesis testing vs estimation

April 3, 2018
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I was looking at my daughter’s statistics homework recently, and there were a pair of questions about testing the level of lead in drinking water. One question concerned testing whether the water was safe, and the other concerned testing whether the water was unsafe. There’s something bizarre, even embarrassing, about this. You want to do […]

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More on the Problem with Bayesian Model Averaging

December 10, 2017
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I blogged earlier on a problem with Bayesian model averaging (BMA) and gave some links to new work that chips away at it. The interesting thing about that new work is that it stays very close to traditional BMA while acknowledging that all models are m...

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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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