I was grading my Master projects this morning and came upon this graph: which compares the variability of an importance-sampling estimator versus its self-normalised alternative… This is an interesting case in that self-normalisation does considerably degrade the quality of the approximation in that setting. In other cases, self-normalisation may bring ... [Read more...]

An X’idated reader of Monte Carlo Statistical Methods had trouble with our Example 3.13, the very one our academic book reviewer disliked so much as to “diverse [sic] a 2 star”. The issue is with computing the integral when f is the Student’s t(5) distribution density. In our book, we ... [Read more...]

I just got the following email from PNAS about our Lack of confidence in ABC model choice. Editor's Remarks to Author: both referees now find the manuscript acceptable for publication as do I. Each suggests small changes which I encourage the authors to make prior to having the manuscript go ... [Read more...]

Following the comments on our earlier submission to PNAS, we have written (and re-arXived) a revised version where we try to spell out (better) the distinction between ABC point (and confidence) estimation and ABC model choice, namely that the problem was at another level for Bayesian model choice (using posterior ... [Read more...]

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In the previous days I have received several emails asking for clarification of the effective sample size derivation in “Introducing Monte Carlo Methods with R” (Section 4.4, pp. 98-100). Formula (4.3) gives the Monte Carlo estimate of the variance of a self-normalised importance sampling estimator (note the change from the original version ... [Read more...]

Today and tomorrow, I am attending the annual Italian statistical society meeting. While I appreciate very much the invitation, as well as the opportunity to walk through  Padova and Venezia for a short (and alas rainy!) hour on the way there (leaving home at 8am, walking in Venezia at noon!), ... [Read more...]