Publications by xi'an

where did the normalising constants go?! [part 1]

10.03.2014

When listening this week to several talks in Banff handling large datasets or complex likelihoods by parallelisation, splitting the posterior as and handling each term of this product on a separate processor or thread as proportional to a probability density, then producing simulations from the mi‘s and attempting at deriving simulations from...

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where did the normalising constants go?! [part 2]

11.03.2014

Coming (swiftly and smoothly) back home after this wonderful and intense week in Banff, I hugged my loved ones,  quickly unpacked, ran a washing machine, and  then sat down to check where and how my reasoning was wrong. To start with, I experimented with a toy example in R: # true target is (x^.7(1-x)^.3) (x^1.3 (1-x)^1.7) # ie a Beta(3,3) dist...

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Approximate Bayesian model choice

16.03.2014

The above is the running head of the arXived paper with full title “Implications of  uniformly distributed, empirically informed priors for phylogeographical model selection: A reply to Hickerson et al.” by Oaks, Linkem and Sukuraman. That I (again) read in the plane to Montréal (third one in this series!, and last because I also watched th...

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sliced Poisson

17.03.2014

One of my students complained that his slice sampler of a Poisson distribution was not working when following the instructions in Monte Carlo Statistical Methods (Exercise 8.5). This puzzled me during my early morning run and I checked on my way back, even before attacking the fresh baguette I had brought from the bakery… The following R code i...

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fine-sliced Poisson [a.k.a. sashimi]

19.03.2014

As my student Kévin Guimard had not mailed me his own Poisson slice sampler of a Poisson distribution, I could not tell why the code was not working! My earlier post prompted him to do so and a somewhat optimised version is given below: nsim = 10^4 lambda = 6 max.factorial = function(x,u){ k = x parf=1 while (parf*u<1){ ...

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Pre-processing for approximate Bayesian computation in image analysis

20.03.2014

With Matt Moores and Kerrie Mengersen, from QUT, we wrote this short paper just in time for the MCMSki IV Special Issue of Statistics & Computing. And arXived it, as well. The global idea is to cut down on the cost of running an ABC experiment by removing the simulation of a humongous state-space vector, as in Potts and hidden Potts model, and re...

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Le Monde puzzle [#857]

21.03.2014

A rather bland case of Le Monde mathematical puzzle : Two positive integers x and y are turned into s=x+y and p=xy. If Sarah and Primrose are given S and P, respectively, how can the following dialogue happen? I am sure you cannot find my number Now you told me that, I can, it is 46. and what are the values of x and y? In the original version,...

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MCMC on zero measure sets

23.03.2014

Simulating a bivariate normal under the constraint (or conditional to the fact) that x²-y²=1 (a non-linear zero measure curve in the 2-dimensional Euclidean space) is not that easy: if running a random walk along that curve (by running a random walk on y and deducing x as x²=y²+1 and accepting with a Metropolis-Hastings ratio based on the biv...

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Bayesian Data Analysis [BDA3]

27.03.2014

Andrew Gelman and his coauthors, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin, have now published the latest edition of their book Bayesian Data Analysis. David and Aki are newcomers to the authors’ list, with an extended section on non-linear and non-parametric models. I have been asked by Sam Behseta to write a review of t...

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Bayesian Data Analysis [BDA3 – part #2]

30.03.2014

Here is the second part of my review of Gelman et al.’ Bayesian Data Analysis (third edition): “When an iterative simulation algorithm is “tuned” (…) the iterations will not in general converge to the target distribution.” (p.297) Part III covers advanced computation, obviously including MCMC but also model approximations like varia...

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