Blog Archives

Approximate Bayesian model choice

March 16, 2014
By
Approximate Bayesian model choice

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

Read more »

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

March 11, 2014
By
where did the normalising constants go?! [part 2]

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: and (of course!) it produced the

Read more »

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

March 10, 2014
By
where did the normalising constants go?! [part 1]

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 the original product,

Read more »

Advances in scalable Bayesian computation [day #4]

March 7, 2014
By
Advances in scalable Bayesian computation [day #4]

Final day of our workshop Advances in Scalable Bayesian Computation already, since tomorrow morning is an open research time ½ day! Another “perfect day in paradise”, with the Banff Centre campus covered by a fine snow blanket, still falling…, and making work in an office of BIRS a dream-like moment. Still looking for a daily theme,

Read more »

Advances in scalable Bayesian computation [day #3]

March 6, 2014
By
Advances in scalable Bayesian computation [day #3]

We have now gone over the midpoint of our workshop Advances in Scalable Bayesian Computation with three talks in the morning and an open research or open air afternoon. (Maybe surprisingly I chose to stay indoors and work on a new research topic rather than trying cross-country skiing!) If I must give a theme for

Read more »

Advances in scalable Bayesian computation [day #2]

March 5, 2014
By
Advances in scalable Bayesian computation [day #2]

And here is the second day of our workshop Advances in Scalable Bayesian Computation gone! This time, it sounded like the “main” theme was about brains… In fact, Simon Barthelmé‘s research originated from neurosciences, while Dawn Woodard dissected a brain (via MRI) during her talk! (Note that the BIRS website currently posts Simon’s video as

Read more »

Advances in scalable Bayesian computation [day #1]

March 4, 2014
By
Advances in scalable Bayesian computation [day #1]

This was the first day of our workshop Advances in Scalable Bayesian Computation and it sounded like the “main” theme was probabilistic programming, in tune with my book review posted this morning. Indeed, both Vikash Mansinghka and Frank Wood gave talks about this concept, Vikash detailing the specifics of a new programming language called Venture

Read more »

Foundations of Statistical Algorithms [book review]

February 27, 2014
By
Foundations of Statistical Algorithms [book review]

There is computational statistics and there is statistical computing. And then there is statistical algorithmic. Not the same thing, by far. This 2014 book by Weihs, Mersman and Ligges, from TU Dortmund, the later being also a member of the R Core team, stands at one end of this wide spectrum of techniques required by

Read more »

Nonlinear Time Series just appeared

February 25, 2014
By
Nonlinear Time Series just appeared

My friends Randal Douc and Éric Moulines just published this new time series book with David Stoffer. (David also wrote Time Series Analysis and its Applications with Robert Shumway a year ago.) The books reflects well on the research of Randal and Éric over the past decade, namely convergence results on Markov chains for validating

Read more »

evaluating stochastic algorithms

February 19, 2014
By
evaluating stochastic algorithms

Reinaldo sent me this email a long while ago Could you recommend me a nice reference about measures to evaluate stochastic algorithms (in particular focus in approximating posterior distributions). and I hope he is still reading the ‘Og, despite my lack of prompt reply! I procrastinated and procrastinated in answering this question as I did not

Read more »