As we were completing our arXiv summary about ABC model choice, we were helpfully pointed to a recent CRiSM tech. report by X. Didelot, R. Everitt, A. Johansen and D. Lawson on Likelihood-free estimation of model evidence. This paper is quite related to our study of the performances of the ABC approximation to the Bayes
Thanks to everyone who attended yesterday's webinar, "Portfolio design, optimization and stability analysis", presented by Diethelm Würtz of the Rmetrics Association and sponsored by Revolution Analytics, Sybase, Finance Online and NeuralTechSoft. (And thanks in particular for your patience for the last start -- in a perfect demonstration of Murphy's law a microphone failed moments before the scheduled start.) If...
A few weeks ago I attended the NYC Data Visualization and Infographics meetup, which included a talk by Junk Charts blogger Kaiser Fung. Given the topic of his blog, I was a bit shocked that the central theme of his talk was comparing good and bad word clouds. He even stated that the
In this post I offer an alternative function for boxplot, which will enable you to label outlier observations while handling complex uses of boxplot.
Details of the inaugural ZurichR and GenevaR meetings are as follows:
This short post is to share an R tip Pierre recently gave me. When you need to store values sequentially (typically inside a loop), it’s more far efficient to create the whole vector (or matrix) and to fill it, rather than to concatenate the values to your current vector (or matrix). In terms of allocation
This may sound like a paradoxical title given my recent production in this area of ABC approximations, especially after the disputes with Alan Templeton, but I have come to the conclusion that ABC approximations to the Bayes factor are not to be trusted. When working one afternoon in Park City with Jean-Michel and Natesh Pillai