Poincar\'e and log-Sobolev inequalities for mixtures

This work studies mixtures of probability measures on $\mathbb{R}^n$ and gives bounds on the Poincar\'e and the log-Sobolev constant of two-component mixtures provided that each component satisfies the functional inequality, and both components are close in the $\chi^2$-distance. The estimation of those constants for a mixture can be far more subtle than it is for its parts. Even mixing Gaussian measures may produce a measure with a Hamiltonian potential possessing multiple wells leading to metastability and large constants in Sobolev type inequalities. In particular, the Poincar\'e constant stays bounded in the mixture parameter whereas the log-Sobolev may blow up as the mixture ratio goes to $0$ or $1$. This observation generalizes the one by Chafa\"i and Malrieu to the multidimensional case. The behavior is shown for a class of examples to be not only a mere artifact of the method.Comment: 13 page

On the existence of classical solution to the steady flows of generalized Newtonian fluid with concentration dependent power-law index

Steady flows of an incompressible homogeneous chemically reacting fluid are described by a coupled system, consisting of the generalized Navier--Stokes equations and convection - diffusion equation with diffusivity dependent on the concentration and the shear rate. Cauchy stress behaves like power-law fluid with the exponent depending on the concentration. We prove the existence of a classical solution for the two dimensional periodic case whenever the power law exponent is above one and less than infinity

Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains

Score matching is an approach to learning probability distributions parametrized up to a constant of proportionality (e.g. Energy-Based Models). The idea is to fit the score of the distribution, rather than the likelihood, thus avoiding the need to evaluate the constant of proportionality. While there's a clear algorithmic benefit, the statistical "cost'' can be steep: recent work by Koehler et al. 2022 showed that for distributions that have poor isoperimetric properties (a large Poincar\'e or log-Sobolev constant), score matching is substantially statistically less efficient than maximum likelihood. However, many natural realistic distributions, e.g. multimodal distributions as simple as a mixture of two Gaussians in one dimension -- have a poor Poincar\'e constant. In this paper, we show a close connection between the mixing time of an arbitrary Markov process with generator $\mathcal{L}$ and an appropriately chosen generalized score matching loss that tries to fit $\frac{\mathcal{O} p}{p}$. If $\mathcal{L}$ corresponds to a Markov process corresponding to a continuous version of simulated tempering, we show the corresponding generalized score matching loss is a Gaussian-convolution annealed score matching loss, akin to the one proposed in Song and Ermon 2019. Moreover, we show that if the distribution being learned is a finite mixture of Gaussians in $d$ dimensions with a shared covariance, the sample complexity of annealed score matching is polynomial in the ambient dimension, the diameter the means, and the smallest and largest eigenvalues of the covariance -- obviating the Poincar\'e constant-based lower bounds of the basic score matching loss shown in Koehler et al. 2022. This is the first result characterizing the benefits of annealing for score matching -- a crucial component in more sophisticated score-based approaches like Song and Ermon 2019.Comment: 39 page

Contributions à l'étude de modèles biologiques, d'inégalités fonctionnelles, et de matrices aléatoires

The presented works concern three autonomous topics :(1) Biological models and Statistics : comparmental models, population pharmacokinetics and pharmacodynamics, estimators for stochastic inverse problems, nonlinear mixed effects models, mixture models, EM and ICF type algorithms, graphical covariance models, modelling in Cancerology, point processes and particles, queueing systems, Feynman-Kac formulas(2) Functional inequalities: Sobolev type inequalities, concentration of measure, isoperimetry, role of convexity in entropic inequalities, tensorization, heat kernels, Heisenberg group and hypoelliptic dynamics, queueing systems, mixtures of distributions (3) Random matrices: spectrum of random Markov matrices, graphs with random weights, Wigner, Marchenko-Pastur, and Girko-Bai type theorems, convergence of extremal eigenvalues, rank one deformations.The most frequent concept here is the notion of Markov dynamics. In the first part, the compartmental models give rise to such dynamics. The second part is related to the geometry and trend to equilibrium of Markov dynamics. The third part is devoted to random Markov dynamics. However, these three parts cannot be reduced to the study of certain aspects of Markov dynamics. The content ranges from concrete applications to abstract theoretical problems, and makes use of various concepts and techniques from Mathematical Analysis, Probability Theory and Statistics.Les travaux présentés concernent trois thématiques autonomes :(1) Modèles biologiques et statistique : modèles compartimentaux, pharmacocinétique et pharmacodynamie de population, estimateurs pour problèmes inverses stochastiques, modèles non-linéaires à effets mixtes, modèles de mélanges, algorithmes de type EM et ICF, modèles graphiques de covariance, modélisation en cancérologie, processus ponctuels, particules, files d'attentes, renormalisation de processus markoviens inhomogènes et formules de Feynman-Kac(2) Inégalités fonctionnelles : inégalités de type Sobolev, concentration de la mesure, isopérimétrie rôle de la convexité dans les inégalités entropiques, tensorisation, noyau de la chaleur, groupe d'Heisenberg et dynamiques hypoelliptiques, files d'attentes, mélanges de lois (3) Matrices aléatoires : spectre des matrices markoviennes aléatoires, graphes à poids aléatoires, théorèmes de type Wigner, Marchenko-Pastur, et Girko-Bai, convergence des valeurs propres extrémales, déformations de rang un.Le concept le plus récurrent ici est celui de dynamique markovienne. Dans la première partie, ce sont les modèles à compartiments de la pharmacologie qui sont liés à de telles dynamiques. La seconde partie traite d'inégalités fonctionnelles associées à la vitesse et à la géométrie de dynamiques markoviennes. Enfin, la troisième partie traite de dynamiques markoviennes aléatoires. Ces trois parties ne se réduisent pas à l'étude de facettes de problèmes markoviens. Leur contenu balaye un spectre à la fois théorique et appliqué, et met en oeuvre des techniques et des concepts variés issus de l'analyse, des probabilités, et de la statistique

Die Eyring-Kramer-Formel für Poincaré- und logarithmische Sobolev-Ungleichungen

The topic of this thesis is a diffusion process on a potential landscape which is given by a smooth Hamiltonian function in the regime of small noise. The work provides a new proof of the Eyring-Kramers formula for the Poincaré inequality of the associated generator of the diffusion. The Poincaré inequality characterizes the spectral gap of the generator and establishes the exponential rate of convergence towards equilibrium in the L²-distance. This result was first obtained by Bovier et. al. in 2004 relying on potential theory. The presented approach in the thesis generalizes to obtain also asymptotic sharp estimates of the constant in the logarithmic Sobolev inequality. The optimal constant in the logarithmic Sobolev inequality characterizes the convergence rate to equilibrium with respect to the relative entropy, which is a stronger distance as the L²-distance and slightly weaker than the L¹-distance. The optimal constant has here no direct spectral representation. The proof makes use of the scale separation present in the dynamics. The Eyring-Kramers formula follows as a simple corollary from the two main results of the work: The first one shows that the associated Gibbs measure restricted to a basin of attraction has a good Poincaré and logarithmic Sobolev constants providing the fast convergence of the diffusion to metastable states. The second main ingredient is a mean-difference estimate. Here a weighted transportation distance is used. It contains the main contribution to the Poincaré and logarithmic Sobolev constant, resulting from exponential long waiting times of jumps between metastable states of the diffusion

Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions

Poincaré inequalities are ubiquitous in probability and analysis and have various applications in statistics (concentration of measure, rate of convergence of Markov chains). The Poincaré constant, for which the inequality is tight, is related to the typical convergence rate of diffusions to their equilibrium measure. In this paper, we show both theoretically and experimentally that, given sufficiently many samples of a measure, we can estimate its Poincaré constant. As a by-product of the estimation of the Poincaré constant, we derive an algorithm that captures a low dimensional representation of the data by finding directions which are difficult to sample. These directions are of crucial importance for sampling or in fields like molecular dynamics, where they are called reaction coordinates. Their knowledge can leverage, with a simple conditioning step, computational bottlenecks by using importance sampling techniques