Posts Tagged ‘ Bayesian statistics ’

A look at Bayesian statistics

September 3, 2012
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A look at Bayesian statistics

An introduction to Bayesian analysis and why you might care. Fight club The subject of statistics is about how to learn.  Given that it is about the unknown, it shouldn’t be surprising that there are deep differences of opinion on how to go about doing it (in spite of the stereotype that statisticians are accountants … Continue reading...

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ASA fellows

May 12, 2012
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ASA fellows

Being freshly elected ASA Fellow (yay!), I just received the list of 2012 ASA Fellows. Among whose, let me mention Sudipto Banerjee, University of Minnesota, Minneapolis, Minnesota, elected “For theoretical, methodological and applied research in spatiotemporal statistical modeling, especially as applied to problems in environmetrics, ecology, occupational health, agriculture and economics, for professional work at

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1500th, 3000th, &tc

January 7, 2012
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1500th, 3000th, &tc

As the ‘Og reached its 1500th post and 3000th comment at exactly the same time, a wee and only mildly interesting Sunday morning foray in what was posted so far and attracted the most attention (using the statistics provided by wordpress). The most visited posts: Title Views Home page 203,727 In{s}a(ne)!! 7,422 “simply start over

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quantum forest

December 1, 2011
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quantum forest

Thanks to a link on R-bloggers, I was introduced to Luis Apiolaza’s blog, Quantum Forest, which covers data analyses and R comments he encounters in his research as a quantitative forester/geneticist. And he works at the University of Canterbury, Christchurch, where I first taught from Bayesian Core in 2006. Which may be why he chose

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Bayesian modeling using WinBUGS

November 6, 2011
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Bayesian modeling using WinBUGS

Yes, yet another Bayesian textbook: Ioannis Ntzoufras’ Bayesian modeling using WinBUGS was published in 2009 and it got an honourable mention at the 2009 PROSE Award. (Nice acronym for a book award! All the mathematics books awarded that year were actually statistics books.) Bayesian modeling using WinBUGS is rather similar to the more recent Bayesian

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Bayesian ideas and data analysis

October 30, 2011
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Bayesian ideas and data analysis

Here is another Bayesian textbook that appeared recently. I read it in the past few days and, despite my obvious biases and prejudices, I liked it very much! It has a lot in common (at least in spirit) with our Bayesian Core, which may explain why I feel so benevolent towards Bayesian ideas and

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understanding computational Bayesian statistics: a reply from Bill Bolstad

October 23, 2011
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understanding computational Bayesian statistics: a reply from Bill Bolstad

Bill Bolstad wrote a reply to my review of his book Understanding computational Bayesian statistics last week and here it is, unedited except for the first paragraph where he thanks me for the opportunity to respond, “so readers will see that the book has some good features beyond having a “nice cover”.” (!) I simply processed

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understanding computational Bayesian statistics

October 9, 2011
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understanding computational Bayesian statistics

I have just finished reading this book by Bill Bolstad (University of Waikato, New Zealand) which a previous ‘Og post pointed out when it appeared, shortly after our Introducing Monte Carlo Methods with R. My family commented that the cover was nicer than those of my own books, which is true. Before I launch into

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Bayesian Models with Censored Data: A comparison of OLS, tobit and bayesian models

September 17, 2011
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Bayesian Models with Censored Data: A comparison of OLS, tobit and bayesian models

The following R code models a censored dependent variable (in this case academic aptitude) using a traditional least squares, tobit, and Bayesian approaches.  As depicted below, the OLS estimates (blue) for censored data are inconsistent and will ...

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Elements of Bayesian Econometrics

September 16, 2011
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Elements of Bayesian Econometrics

 posterior = (likelihood x prior) / integrated likelihoodThe combination of a prior distribution and a likelihood function is utilized to produce a posterior distribution.  Incorporating information from both the prior distribution and the likelihood function leads to a reduction in variance and an improved estimator. As n→...

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