Bayes factors and the geometry of discrete hierarchical loglinear models

A standard tool for model selection in a Bayesian framework is the Bayes factor which compares the marginal likelihood of the data under two given different models. In this paper, we consider the class of hierarchical loglinear models for discrete data given under the form of a contingency table with multinomial sampling. We assume that the Diaconis-Ylvisaker conjugate prior is the prior distribution on the loglinear parameters and the uniform is the prior distribution on the space of models. Under these conditions, the Bayes factor between two models is a function of their prior and posterior normalizing constants. These constants are functions of the hyperparameters $(m,\alpha)$ which can be interpreted respectively as marginal counts and the total count of a fictive contingency table. We study the behaviour of the Bayes factor when $\alpha$ tends to zero. In this study two mathematical objects play a most important role. They are, first, the interior $C$ of the convex hull $\bar{C}$ of the support of the multinomial distribution for a given hierarchical loglinear model together with its faces and second, the characteristic function $\mathbb{J}_C$ of this convex set $C$. We show that, when $\alpha$ tends to 0, if the data lies on a face $F_i$ of $\bar{C_i},i=1,2$ of dimension $k_i$, the Bayes factor behaves like $\alpha^{k_1-k_2}$. This implies in particular that when the data is in $C_1$ and in $C_2$, i.e. when $k_i$ equals the dimension of model $J_i$, the sparser model is favored, thus confirming the idea of Bayesian regularization.Comment: 37 page

Dirichlet random walks

This article provides tools for the study of the Dirichlet random walk in $\mathbb{R}^d$. By this we mean the random variable $W=X_1\Theta_1+\cdots+X_n\Theta_n$ where $X=(X_1,\ldots,X_n) \sim \mathcal{D}(q_1,\ldots,q_n)$ is Dirichlet distributed and where $\Theta_1,\ldots \Theta_n$ are iid, uniformly distributed on the unit sphere of $\mathbb{R}^d$ and independent of $X.$ In particular we compute explicitely in a number of cases the distribution of $W.$ Some of our results appear already in the literature, in particular in the papers by G\'erard Le Ca\"{e}r (2010, 2011). In these cases, our proofs are much simpler from the original ones, since we use a kind of Stieltjes transform of $W$ instead of the Laplace transform: as a consequence the hypergeometric functions replace the Bessel functions. A crucial ingredient is a particular case of the classical and non trivial identity, true for $0\leq u\leq 1/2$:$$_2F_1(2a,2b;a+b+\frac{1}{2};u)= \_2F_1(a,b;a+b+\frac{1}{2};4u-4u^2).$$ We extend these results to a study of the limits of the Dirichlet random walks when the number of added terms goes to infinity, interpreting the results in terms of an integral by a Dirichlet process. We introduce the ideas of Dirichlet semigroups and of Dirichlet infinite divisibility and characterize these infinite divisible distributions in the sense of Dirichlet when they are concentrated on the unit ball of $\mathbb{R}^d.$ {4mm}\noindent \textsc{Keywords:} Dirichlet processes, Stieltjes transforms, random flight, distributions in a ball, hyperuniformity, infinite divisibility in the sense of Dirichlet. {4mm}\noindent \textsc{AMS classification}: 60D99, 60F99

The limiting behavior of some infinitely divisible exponential dispersion models

Consider an exponential dispersion model (EDM) generated by a probability $\mu$ on $[0,\infty )$ which is infinitely divisible with an unbounded L\'{e}vy measure $\nu$. The Jorgensen set (i.e., the dispersion parameter space) is then $\mathbb{R}^{+}$, in which case the EDM is characterized by two parameters: $\theta _{0}$ the natural parameter of the associated natural exponential family and the Jorgensen (or dispersion) parameter $t$. Denote by $EDM(\theta _{0},t)$ the corresponding distribution and let $Y_{t}$ is a r.v. with distribution $EDM(\theta_0,t)$. Then if $\nu ((x,\infty ))\sim -\ell \log x$ around zero we prove that the limiting law $F_0$ of $Y_{t}^{-t}$ as $t\rightarrow 0$ is of a Pareto type (not depending on $\theta_0$) with the form $F_0(u)=0$ for $u<1$ and $1-u^{-\ell }$ for $u\geq 1$. Such a result enables an approximation of the distribution of $Y_{t}$ for relatively small values of the dispersion parameter of the corresponding EDM. Illustrative examples are provided.Comment: 8 page

Random walks in the hyperbolic plane and the Minkowski question mark function

Si consideri il gruppo delle matrici invertibili 2x2 con coefficienti interi che agisce sul semipiano superiore del piano complesso mediante le corrispondenti trasformazioni di Mobius. Consideriamo una passeggiata casuale sul piano costruita scegliendo a caso tra nove matrici. Dimostriamo che la camminata casuale converge in legge ad una variabile casuale la cui la distribuzione è collegata in un senso semplice alla funzione di Minkowski ? Varie proprietà di questa legge limite possono essere ottenute da questa analisi.Consider the group of 2x2 invertible matrices with integer coefficients acting on the complex upper half-plane by the corresponding Mobius transformations. We consider a random walk on the plane constructed by choosing at random among nine matrices. We show that the random walk converges in law to a random variable whose distribution is related in a simple way to Minkowski's ? function. Various properties of this limiting law can be obtained from this analysis