## Comments for València 9

**F**ollowing discussions at CREST, we have contributed comments on the following papers

**Bernardo, José M.** (Universitat de València, Spain)

* Integrated objective Bayesian estimation and hypothesis testing. *[discussion]

**Consonni, Guido** (Università di Pavia, Italy)

* On moment priors for Bayesian model choice with applications to directed acyclic graphs.* [discussion]

**Frühwirth-Schnatter, Sylvia** (Johannes Kepler Universität Linz, Austria)

* Bayesian variable selection for random intercept modeling of Gaussian and non-Gaussian data. *[discussion]

**Huber, Mark** (Claremont McKenna College, USA)

* Using TPA for Bayesian inference.* [discussion]

**Lopes, Hedibert** (University of Chicago, USA)

* Particle learning for sequential Bayesian computation.* [discussion]

**Polson, Nicholas ** (University of Chicago, USA)

* Shrink globally, act locally: Sparse Bayesian regularization and prediction.* [discussion]

**Wilkinson, Darren** (University of Newcastle, UK)

* Parameter inference for stochastic kinetic models of bacterial gene regulation: a Bayesian approach to systems biology.* [discussion]

(with a possible incoming update on Mark Huber’s comments if we manage to get the simulations running in due time).

October 18, 2011 at 12:13 am

[...] to make any sense to a decision analyst. Or even to a statistician. We discussed earlier the València 9 paper of Guido Consonni, in connection with more realistic loss functions. Also the authors seem to [...]

September 27, 2010 at 12:13 am

[...] paper on Riemann manifold Langevin and Hamiltonian Monte Carlo methods and I hope we will again produce a joint arXiv preprint out of our comments. (The above picture is reproduced from Radford [...]

August 22, 2010 at 12:20 am

[...] in the fields of statistics and econometrics. Arnold was 83 and, although I had met him in several Valencia meetings—including one in Alicante where we sat together for breakfast with Persi Diaconis and [...]

June 27, 2010 at 12:10 am

[...] wonder how closely related the second (volume tesselation) algorithm is to Huber and Schott’s TPA algorithm, in the sense that TPA also requires of a “smaller” [...]

June 24, 2010 at 5:59 pm

Clearly I need more education in measure theory.

June 24, 2010 at 6:20 pm

Corey, this is a subtlety of measure theory and a lot of people are convinced that MAP estimators are associated with 0-1 losses. Actually, in Bayesian Choice (Section 4.1.2,

MAP estimator), I actually justify the use of the MAP via a limit of estimators associated with a sequence of losses, but this is only an approximation. Strictly speaking, the MAP estimator is not a decision-theoretic object!June 24, 2010 at 1:15 am

[...] reading Christian Robert’s blog highlighting some of the discussion of the invited program for Valencia 9, I realized that the entire invited program was available on the Valencia website. For anybody who [...]

June 23, 2010 at 3:37 am

I noticed that in your comments on the “Shrink globally, act locally” article, you wrote that MAP estimators do not fit in a decision-theoretic framework. If the loss function is -delta(estimate – true_val), (mathematically capturing the the English idiomatic expression “a miss is as good as a mile“) then the expected loss of the estimate is the negative of the probability density, and is minimized by the MAP estimator. It’s true that that’s a brutal loss function, but sometimes life isn’t fair. How does the above argument fail?

June 24, 2010 at 6:47 am

The problem with the delta loss function in continuous settings is that if you consider it as an indicator function (1 everywhere except at a specific value) the expectation of the loss function is always one by measure theoretic arguments: the value at a given point does not matter. To make a difference at the MAP, you would need to use the delta function as a distribution, which is not acceptable… Besides, the MAP depends on the dominating measure, another difficulty…