Posts Tagged ‘ Bayesian statistics ’

Nomen omen

December 9, 2016
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Nomen omen

After resisting this for way too long, I've finally decided it was time to release more widely a couple of the R packages I've been working on $-$ I've put them on GitHub, hence the mug...In both cases, while I think the packages do work nicely, I am s...

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Using Stan in an agent-based model: Simulation suggests that a market could be useful for building public consensus on climate change

December 5, 2016
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Using Stan in an agent-based model:  Simulation suggests that a market could be useful for building public consensus on climate change

Jonathan Gilligan writes: I’m writing to let you know about a preprint that uses Stan in what I think is a novel manner: Two graduate students and I developed an agent-based simulation of a prediction market for climate, in which traders buy and sell securities that are essentially bets on what the global average temperature […] The post Using Stan in an agent-based model: Simulation suggests that a market could…

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Good stuff around

December 2, 2016
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Good stuff around

Lately, I've been publicising quite heavily our Summer school and new MSc, but of course, we're not the only one to plan for interesting things worth mentioning $-$ well, of course this is highly subjective... But then again, this blog is (mainly)...

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Interesting epi paper using Stan

November 30, 2016
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Jon Zelner writes: Just thought I’d send along this paper by Justin Lessler et al. Thought it was both clever & useful and a nice ad for using Stan for epidemiological work. Basically, what this paper is about is estimating the true prevalence and case fatality ratio of MERS-CoV [Middle East Respiratory Syndrome Coronavirus Infection] […] The post Interesting epi paper using Stan appeared first on Statistical Modeling, Causal Inference,…

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OK, sometimes the concept of “false positive” makes sense.

November 28, 2016
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OK, sometimes the concept of “false positive” makes sense.

Paul Alper writes: I know by searching your blog that you hold the position, “I’m negative on the expression ‘false positives.'” Nevertheless, I came across this. In the medical/police/judicial world, false positive is a very serious issue: $2 Cost of a typical roadside drug test kit used by police departments. Namely, is that white powder […] The post OK, sometimes the concept of “false positive” makes sense. appeared first on…

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Discussion on overfitting in cluster analysis

November 25, 2016
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Discussion on overfitting in cluster analysis

Ben Bolker wrote: It would be fantastic if you could suggest one or two starting points for the idea that/explanation why BIC should naturally fail to identify the number of clusters correctly in the cluster-analysis context. Bob Carpenter elaborated: Ben is finding that using BIC to select number of mixture components is selecting too many […] The post Discussion on overfitting in cluster analysis appeared first on Statistical Modeling, Causal…

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Abraham Lincoln and confidence intervals

November 23, 2016
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Abraham Lincoln and confidence intervals

Our recent discussion with mathematician Russ Lyons on confidence intervals reminded me of a famous logic paradox, in which equality is not as simple as it seems. The classic example goes as follows: Abraham Lincoln is the 16th president of the United States, but this does not mean that one can substitute the two expressions […] The post Abraham Lincoln and confidence intervals appeared first on Statistical Modeling, Causal Inference,…

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How best to partition data into test and holdout samples?

November 22, 2016
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How best to partition data into test and holdout samples?

Bill Harris writes: In “Type M error can explain Weisburd’s Paradox,” you reference Button et al. 2013. While reading that article, I noticed figure 1 and the associated text describing the 50% probability of failing to detect a significant result with a replication of the same size as the original test that was just significant. […] The post How best to partition data into test and holdout samples? appeared first…

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Deep learning, model checking, AI, the no-homunculus principle, and the unitary nature of consciousness

November 21, 2016
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Deep learning, model checking, AI, the no-homunculus principle, and the unitary nature of consciousness

Bayesian data analysis, as my colleagues and I have formulated it, has a human in the loop. Here’s how we put it on the very first page of our book: The process of Bayesian data analysis can be idealized by dividing it into the following three steps: 1. Setting up a full probability model—a joint […] The post Deep learning, model checking, AI, the no-homunculus principle, and the unitary nature…

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Summer School: Bayesian Methods in Health Economics

November 21, 2016
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Summer School: Bayesian Methods in Health Economics

We're finally ready to advertise our new Summer School on Bayesian Methods in Health Economics, in Florence, 12-16 June 2017! This is basically combining the two short courses that we've run in the past few years $-$ the first one on Bayesian modelling...

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