# Category Archives: Bayesian

## reblog: “Tiny Data, Approximate Bayesian Computation and the Socks of Karl Broman”

It’s Rasmus Bååth, in a post and video of which I am very fond: http://www.sumsar.net/blog/2014/10/tiny-data-and-the-socks-of-karl-broman/.

## On differential localization of tumors using relative concentrations of ctDNA. Part 2.

Part 1 of this series introduced the idea of ctDNA and its use for detecting cancers or their resurgence, and proposed a scheme whereby relative concentrations of ctDNA at two or more sites after controlled disturbance might be used to … Continue reading

## On differential localization of tumors using relative concentrations of ctDNA. Part 1.

Like most mammalian tissue, tumors often produce shards of DNA as a byproduct of cell death and fracture. This circulating tumor DNA is being studied as a means of detecting tumors or their resurgence after treatment. (See also a Q&A … Continue reading

## Deep Recurrent Learning Networks

(Also known to statisticians as deep exponential families.) Large scale deep learning Four easy lessons on Deep Learning from Google

## “The Bayesian Second Law of Thermodynamics” (Sean Carroll, and collaborators)

http://www.preposterousuniverse.com/blog/2015/08/11/the-bayesian-second-law-of-thermodynamics/ See also.

## Comprehensive and compact tutorial on Petris’ DLM package in R; with an update about Helske’s KFAS

A blogger named Lalas produced on Quantitative Thoughts a very comprehensive and compact tutorial on the R package dlm by Petris. I use dlm a lot. Unfortunately, Lalas does not give details on how the SVD is used. They do … Continue reading

## Destroying the Most Persistent Scientific Myth In America – Dan’s Wild Wild Science Journal – AGU Blogosphere

Destroying the Most Persistent Scientific Myth In America – Dan's Wild Wild Science Journal – AGU Blogosphere.

## “Cauchy Distribution: Evil or Angel?” (from Xian)

Cauchy Distribution: Evil or Angel?. From Professor Christian Robert.

## “… the most patronizing start to an answer I have ever received …”

Professor Christian Robert tries to help out a student of MCMC on Cross Validated and earns the comment that his help had “the most patronizing start to an answer I have ever received“. I learned a new term: primitivus petitor.

## “A vignette on Metropolis” (Christian Robert)

Originally posted on Xi'an's Og:

Over the past week, I wrote a short introduction to the Metropolis-Hastings algorithm, mostly in the style of our Introduction to Monte Carlo with R book, that is, with very little theory and…

## “Unbiased Bayes for Big Data: Path of partial posteriors” (Christian Robert)

Unbiased Bayes for Big Data: Path of partial posteriors.

## Dynamic Linear Models package, dlmodeler

I’m checking out the dlmodeler package in R for a work project. It is accompanied by textbooks, G. Petris, S. Petrone, P. Campagnoli, Dynamic Linear Models with R, Springer, 2009 and J. Durbin, S. J. Koopman, Time Series Analysis by … Continue reading

## Markov Chain Monte Carlo methods and logistic regression

This post could also be subtitled “Residual deviance isn’t the whole story.” My favorite book on logistic regression is by Dr Joseph Hilbe, Logistic Regression Models, CRC Press, 2009, Chapman & Hill. It is a solidly frequentist text, but its … Continue reading

## Bayesian change-point analysis for global temperatures, 1850-2010

Professor Peter Congdon reports on two Bayesian models for global temperature shifts in his textbook, Applied Bayesian Modelling, as “Example 6.12: Global temperatures, 1850-2010”, on pages 252-253. A direct link is available online. The first is apparently original with Congdon, … Continue reading

## “Big Data is the new Phrenology”

From mathbabe: Big Data is the new phrenology. Excerpt: Here’s the thing. What we’ve got is a new kind of awful pseudo-science, which replaces measurements of skulls with big data. There’s no reason to think this stuff is any less … Continue reading

## R vs Python: Practical Data Analysis

R vs Python: Practical Data Analysis (Nonlinear Regression).

## Christian Robert on the amazing Gibbs sampler

Professor Christian Robert remarks on the amazing Gibbs sampler. Implicitly he’s also underscoring the power of properly done Bayesian computational analysis. For here we have a problem with a posterior distribution having two strong modes, so a point estimate, like … Continue reading

## Christian Robert on Alan Turing

Alan Turing Institute. See Professor Robert’s earlier post on Turing, too.

## engineering and understanding with stable models

Stable distributions or Lévy -stable models is a class of probability distributions which contains the Gaussian, the Cauchy (or Lorentz), and the Lévy distribution. They are parameterized by an which is . Values of of 1 or less give distributions … Continue reading

## “a day of mourning”

Professor Christian Robert’s a day of mourning. Update: 2015-01-12, 1352 ET From KAL at The Economist: Update: 2015-01-31, 1736 ET And, finally, Sam Harris, who I applaud:

## Naomi Oreskes and significance testing

Naomi Oreskes has an op-ed in The New York Times today, which intends to defend the severe standards of evidence scientists employ, with special applicability to climate science and their explanation of causation (greenhouse gases produce radiative forcing), attribution (most … Continue reading

## On nested equivalence classes of climate models, ordered by computational complexity

I’m digging into the internals of ABC, for professional and scientific reasons. I’ve linked a great tutorial elsewhere, and argued that this framework, advanced by Wood, and Wilkinson (Robert), and Wilkinson (Darren), and Hartig and colleagues, and Robert and colleagues, … Continue reading

## “[W]e want to model the process as we would simulate it.”

Professor Darren Wilkinson offers a pithy insight on how to go about constructing statistical models, notably hierarchical ones: “… we want to model the process as we would simulate it ….” This appears in his blog post One-way ANOVA with … Continue reading

## struggling with problems already partly solved by others

Climate modelers and models see as their frontier the problem of dealing with spontaneous dynamics in systems such as atmosphere or ocean which are not directly forced by boundary conditions such as radiative forcing due to increased greenhouse gas (“GHG”) … Continue reading

## illustrating particle filters and Bayesian fusion using successive location estimates on the unit circle

Introduction Modern treatments of Bayesian integration to obtain posterior densities often use some form of Markov Chain Monte Carlo (“MCMC”), typically Gibbs sampling. Gibbs works well with many Bayesian hierarchical models. The standard problem-solving situation with these is that a … Continue reading

## “… making a big assumption …”

“That’s making a big assumption.” (This post is a follow-on from an earlier one.) In the colloquial, the phrase means basing an argument on a precondition which is unusual or atypical or offends common sense. When applied to scientific hypotheses, … Continue reading

## Bayesian inference works even in a chaotic or deterministic world

Professor John Geweke, in a Comment on an article by Professor Mark Berliner a bit back (1992), shows how Bayesian inference continues to be a means for expressing subjective uncertainty even in a scheme where there are no stochastics but … Continue reading

## “Can we trust climate models?”

J. C. Hargreaves, J. D. Annan, “Can we trust climate models?”, WIREs Climate Change 2014, 5:435–440. doi: 10.1002/wcc.288. See also D. A. Stainforth, T. Aina, C. Christensen, M. Collins, N. Faull, D. J. Frame, J. A. Kettleborough, S. Knight, A. … Continue reading

## Liddell and Kruschke, on conditional logistic Bayesian estimation

(“Ostracism and fines in a public goods game with accidental contributions: The importance of punishment type”) An overview. The article