Probability, Analysis, and Data Science Seminar

Spring 2022

Wednesdays at 4:10pm-5:10pm (including 10 minutes Q&A) in person (Carver 401) or virtually (via Zoom at https://iastate.zoom.us/j/9974481906), depending on the speaker.

Organizers: Pablo Raúl Stinga and Ruoyu Wu

Mar 2 (4:10pm) Carver 401 and Zoom - Jina Kim (ISU)

Title: From finite to infinite dimensions: functional inequalities for linear diffusions with degenerate noise

Abstract: In this talk, we introduce functional inequalities for specific classes of hypoelliptic stochastic differential equations. In particular, these inequalities include the reverse log-Sobolev inequality and Wang-type Harnack inequality for a large class of linear SDEs with degenerate noise. They are obtained from gradient bounds of the semigroup of the process and making use of the generalized version of the carré du champ operator. From the results we take steps further and also look for quasi-invariance of the same form of SDEs that take values in an infinite-dimensional Hilbert space. This is to gain some type of smoothness of the heat kernel measure of an infinite-dimensional process, and done by using the "projected" bound.

Mar 9 (4:10pm) Zoom - Lee Przybylski (ISU)

Title: The Persistence Landscapes of Affine Fractals

Abstract: We develop a method for calculating the persistence landscapes of affine fractals using the parameters of the corresponding transformations. Given an iterated function system of affine transformations that satisfies a certain compatibility condition, we prove that there exists an affine transformation acting on the space of persistence landscapes which intertwines the action of the iterated function system. This latter affine transformation is a strict contraction and its unique fixed point is the persistence landscape of the affine fractal. We present several examples of the theory as well as confirm the main results through simulations.

Mar 16 Spring Break

Mar 23 (4:10pm) Zoom - Virginia Naibo (Kansas State University)

Title : The Neumann problem in graph Lipschitz domains in the plane.

Abstract: New aspects of the solvability of the classical Neumann boundary value problem in a graph Lipschitz domain in the plane will be presented. When the domain is the upper half-plane and the boundary data is assumed to belong to weighted Lebesgue or weighted Lorentz spaces, it will be shown that the solvability of the Neumann problem in these settings may be characterized in terms of Muckenhoupt weights and related weights, respectively. For a general graph Lipschitz domain $\Omega$, as proved in an unpublished work by E. Fabes and C. Kenig, there exists $\varepsilon>0$ such that the Neumann problem is solvable with data in $L^p(\partial\Omega)$ for $1<p<2+\varepsilon;$ it will be shown that the Neumann problem is solvable at the endpoint $2+\varepsilon$ with data in the Lorentz space $L^{2+\varepsilon}(\partial\Omega).$ Examples of the results in Schwarz-Christoffel Lipschitz domains and related domains will be given. This is joint work with María Jesús Carro (Universidad Complutense de Madrid) and Carmen Ortiz-Caraballo (Universidad de Extremadura).

Apr 6 (4:10pm) Zoom - Guo-Jhen Wu (KTH Royal Institute of Technology)

Title: Quasi-stationary distribution and ergodic control problems

Abstract: We introduce two ergodic control problems that can be used to analyze the quasi-stationary distributions (QSDs) associated with a diffusion process. The two problems are in some sense dual, with one defined in terms of the generator associated with the diffusion process and the other in terms of its adjoint. The first ergodic control problem can be used to characterize the Q-process associated with the QSD, and the cost potential of the second ergodic control problem to characterize the QSD itself. We briefly mention how the control problems can be used to construct numerical approximations of the QSD. This is joint work with Amarjit Budhiraja (University of North Carolina at Chapel Hill), Paul Dupuis (Brown University), and Pierre Nyquist (KTH).

Apr 13 (4:10pm) Zoom - Rodolfo H. Torres (University of California, Riverside)

Title: John-Nirenberg Inequalities and Weight Invariant BMO Spaces

Abstract: We will explore new deep connections between John-Nirenberg inequalities and Muckenhoupt weight invariance for a large class of spaces including or resembling versions of the classical space of functions of Bounded Mean Oscillation (BMO). The results are formulated in a very general framework in which $BMO$-type spaces are constructed using a base of sets, employed also to define weights with respect to a non-negative measure (not necessarily doubling), and an appropriate oscillation functional. This includes as particular cases many different function spaces on geometric settings of interest. As a consequence the weight invariance of several $BMO$ spaces considered in the literature is proved. Most of the invariance results obtained under this unifying approach are new even in the most classical settings. This is joint work with Jarod Hart.

Apr 20 (4:10pm) Zoom - Ruimeng Hu (UCSB)

Title: Convergence of Empirical Measures, Mean-Field Games and Signatures

Abstract: In this talk, we first propose a new class of metrics and show that under such metrics, the convergence of empirical measures in high dimensions is free of the curse of dimensionality, in contrast to Wasserstein distance. Proposed metrics originate from the maximum mean discrepancy, which we generalize by proposing criteria for test function spaces. Examples include RKHS, Barron space, and flow-induced function spaces. One application studies the construction of Nash equilibrium for the homogeneous n-player game by its mean-field limit (mean-field game). Then we discuss mean-field games with common noise and propose a deep learning algorithm based on fictitious play and signatures in rough path theory. The first part of the work collaborates with Jiequn Han and Jihao Long; the second part is the joint work with Ming Min.

Apr 27 (4:10pm) Zoom - Michael Perlmutter (UCLA)

Title: Deep Learning for Data with Geometric Structure

Abstract: The field of Geometric Deep Learning aims to extend the success of Convolutional Neural Networks to data sets that lack a Euclidean, grid-like structure and are more naturally modeled as manifolds or (possibly directed). A major advance in this field has been the rise of graph convolutional networks which extend the success of CNNs to the graph domain by defining convolution either as a localized averaging operation or via the eigendecomposition of the graph Laplacian.

Despite the success of these networks, standard graph convolutional networks also have some limitations. They have difficulty (i) incorporating high-frequency information and long-range dependencies. (ii) handling directed graphs. In my talk, I will talk about how we can overcome these difficulties via (i) the graph and manifold scattering transforms which capture high-frequency information and long-range dependencies via wavelet filters, (ii) MagNet, a directed graph neural net that encodes directional information via a Hermitian matrix known as the magnetic Laplacian.

May 4 (4:10pm) Zoom - Michael Conroy (University of Arizona)

Title: Renewal Theory for Maxima on Trees

Abstract: This talk focuses on the all-time supremum W of the perturbed branching random walk, which is the so-called endogenous solution to the stochastic fixed-point equation known as the high-order Lindley equation. Under certain assumptions (in particular that the paths of each branch of the random walk have negative drift), W satisfies the tail asymptotic P(W > t) ~ He^{-at} for certain constants H, a > 0. Using tools from Markov renewal theory and spine changes of measure for branching processes, we establish the tail asymptotic by analyzing the forward iterations of the map defining the fixed-point equation. As a bonus to this approach, we obtain an unbiased, strongly-efficient, and easy-to-simulate estimator for the rare event probability P(W > t). This talk presents joint work with Bojan Basrak (University of Zagreb), Mariana Olvera-Cravioto (UNC Chapel Hill), and Zbigniew Palmowski (Wroclaw University of Science and Technology).

Sep 8 (4:10pm) Carver 401 and Zoom - David Herzog (ISU)

Title: A functional law of the iterated logarithm for weakly hypoelliptic diffusions

Abstract: This is part two of three talks in the spring and fall. We discuss the method of stochastic characteristics to help motivate laws of the iterated logarithm. This method is often used to solve second-order linear boundary-valued PDEs, such as the Dirichlet or Poisson Problem in a bounded domain in R^n. If the operator L defining the equation is uniformly elliptic in a neighborhood of the domain and the boundary is smooth, then we can always use this method with ease to find an expression for the unique classical solution, provided the data is smooth enough. However, if we drop the ellipticity assumption, say L is only hypoelliptic or "weakly" hypoelliptic (i.e. the randomness is degenerate), then the candidate expression typically makes sense, but it may no longer satisfy the equation. The problem moving from the elliptic to hypoelliptic settings is that the natural elliptic approximation of L may not approximate the defining stochastic characteristics near the boundary, even though the processes defined by these operators are close in a very strong sense. In order to determine when it does approximate L near the boundary, we establish laws of the iterated logarithm for the diffusion at time zero.

Sep 15 (4:10pm) Carver 401 and Zoom - David Herzog (ISU)

Title: A functional law of the iterated logarithm for weakly hypoelliptic diffusions

Abstract: This is part three of three talks in the spring and fall. We discuss the method of stochastic characteristics to help motivate laws of the iterated logarithm. This method is often used to solve second-order linear boundary-valued PDEs, such as the Dirichlet or Poisson Problem in a bounded domain in R^n. If the operator L defining the equation is uniformly elliptic in a neighborhood of the domain and the boundary is smooth, then we can always use this method with ease to find an expression for the unique classical solution, provided the data is smooth enough. However, if we drop the ellipticity assumption, say L is only hypoelliptic or "weakly" hypoelliptic (i.e. the randomness is degenerate), then the candidate expression typically makes sense, but it may no longer satisfy the equation. The problem moving from the elliptic to hypoelliptic settings is that the natural elliptic approximation of L may not approximate the defining stochastic characteristics near the boundary, even though the processes defined by these operators are close in a very strong sense. In order to determine when it does approximate L near the boundary, we establish laws of the iterated logarithm for the diffusion at time zero.

Oct 13 (4:10pm) Zoom - Debankur Mukherjee (Georgia Tech)

Title: Load Balancing Under Strict Compatibility Constraints

Abstract: We will talk about large-scale systems operating under the JSQ(d) policy in the presence of stringent task-server compatibility constraints. Consider a system with N identical single-server queues and M(N) task types, where each server is able to process only a small subset of possible task types. Each arriving task selects d≥2 random servers compatible to its type, and joins the shortest queue among them. The compatibility constraint is naturally captured by a fixed bipartite graph GN between the servers and the task types. When GN is complete bipartite, the mean-field approximation is proven to be accurate as N→∞. However, such dense compatibility graphs are infeasible due to their overwhelming implementation cost and prohibitive storage capacity requirement at the servers. We will characterize a class of sparse compatibility graphs for which the mean-field approximation remains valid asymptotically as N→∞. Both process-level limit and steady-state convergence results will be discussed. This is a joint work with Daan Rutten.

Oct 20 (4:10pm) Zoom - Mikil Foss (University of Nebraska-Lincoln)

Title: Trace Theorems for Non-Differentiable Functions

Abstract: Given a uniformly continuous function on an open domain, there is a unique extension to the boundary that preserves the continuity. The trace operator provides a function that captures the boundary values for this extension. Gagliardo’s trace theorem extends this concept to the Sobolev spaces. There have been generalizations of Gagliardo’s theorem in many directions. Typically, trace theorems require some differentiability of the function in its domain and some regularity of the domain’s boundary. These assumptions ensure a there is a well-defined boundary value function. Moreover, this trace will, itself, possess some differentiability and a certain Lebesgue point property. I will present a trace theorem that provides a well-defined boundary-value function that exists in a fractional Sobolev space and has the Lebesgue point property yet requires no differentiability within the domain and allows very irregular boundaries. The result is motivated by boundary-value problems involving nonlocal operators that are defined for integrable but not necessarily differentiable functions.

Oct 27 (4:25pm) Zoom - Farzad Sabzikar (ISU)

Title: How Does Tempering Affect the Local and Global Properties of Fractional Brownian Motion?

Abstract: In this talk, we discuss the effects of tempering the power-law kernel of the moving average representation of a fractional Brownian motion (fBm) on some local and global properties of this Gaussian stochastic process. Tempered fractional Brownian motion (TFBM) and tempered fractional Brownian motion of the second kind (TFBMII) are the processes that are considered in order to investigate the role of tempering. Tempering does not change the local properties of fBm including the sample paths and p-variation, but it has a strong impact on the Breuer–Major theorem, asymptotic behavior of the third and fourth cumulants of fBm, and the optimal fourth-moment theorem.

Nov 3 (4:10pm) Carver 401 and Zoom - Evan Camrud (ISU)

Title: Bedrossian, Blumenthal, and Punshon-Smith's bounds on Lyapunov exponents for SDEs

Abstract: This will be a literature review talk on a paper that has been very interesting to my research. Bedrossian et al. obtain lower estimates for the maximal and sum Lyapunov exponents of the stochastic Lorenz 96 dynamical system. The positivity of the maximal exponent implies chaos in the dynamics while the negativity of the sum exponent implies Lebesgue volume contraction.

Nov 17 (4:10pm) Zoom - Jaydeb Sarkar (Indian Statistical Institute)

Title: Schur functions - A brief survey

Abstract: The Schur class, denoted by S(D), is the set of all functions analytic and bounded by one in modulus in the open unit disc D in the complex plane. Elements of S(D) are called Schur functions. A classical result going back to Issai Schur states: A function f is a Schur function if and only if f admits a linear fractional transformation (or transfer function realization). Linear fractional transformations are attached with colligation matrices or scattering matrices on Hilbert spaces. Schur's view of bounded analytic functions is one of the most used (and useful) tools in classical and modern complex analysis, function theory, operator theory, electrical network theory, signal processing, linear systems, operator algebras, and image processing (just to name a few).

In the first part of this talk, we will give a brief (but within the span of little more than a century) historic perspective and introduction to Schur theory and discuss its interactions with some classical problems in function theory and operator theory (like Nevanlinna-Pick interpolation). In the second part of the talk, we will review Schur's approach (ubiquity and its complications) to functions of several complex variables from a linear analysis point of view.

Dec 1 (4:10pm) Zoom - Simon Bortz (University of Alabama)

Title: Recent Developments in Parabolic Uniform Rectifiability

Abstract: In the 1980’s harmonic analysis was being rapidly adapted to rough’ settings. Among the important early developments was the proof of the L^2 boundedness of the (principal value) Cauchy integral operator on a Lipschitz graph due to Coifman, McIntosh and Meyer. Soon after Coifman, David and Meyer extended this result to higher dimensions and more general singular integral operators. A natural question arose: Is there a characterization of sets for which all nice singular integral operators are L^2 bounded? David and Semmes found many characterizations of these sets and called them uniformly rectifiable. More recently these sets have played a significant role in potential theory.

In this talk, I will discuss the history of uniformly rectifiable sets and some of my recent work on their parabolic analogs. The parabolic theory presents some obstacles in the form of non-local’ smoothness and, in fact, many of the characterizations of uniform rectifiability fail to have parabolic analogs.

Dec 8 (4:10pm) Zoom - Gideon Simpson (Drexel University)

Title: Nonlocal Diffusions with Additive Noise

Abstract: In this talk I will discuss recent work on infinite dimensional nonlocal diffusions with additive noise. These may be obtained as the continuum limits of noise driven Kuramoto oscillator systems. A well-posedness theory is developed for this infinite dimensional problem, and convergence results are obtained for both the associated semi-discrete and fully discrete problems. This provides a basis for studying the associated metastability problems of the continuum limit Kuramoto system, which may also be viewed as an approximation of a high, but finite, dimensional problem. Ongoing progress on studying the metastability, both numerically and analytically will be presented. Novel challenges in the analysis are also highlighted. This work is in collaboration with Georgi Medvedev (Drexel).

Past Seminars: Spring 2021

Feb 10 (4:10pm) - Ananda Weerasinghe (ISU)

Title: Diffusion approximations and optimal control of processing systems in heavy traffic

Abstract: A running maximum control of a diffusion process is considered. To find an optimal control, we consider the associated two-dimensional degenerate Hamilton-Jacobi-Bellman(HJB) equation and construct a sufficiently smooth solution. This in turn leads to a bounded, feed-back type optimal control.

This diffusion control problem(DCP) is motivated by the service rate control of an infinite-server processing system in heavy traffic where the control mechanism is enforced to limit the storage capacity. The DCP is obtained as the diffusion approximation of the processing system control problem. An asymptotically feed-back type optimal control mechanism for the processing system is obtained using the solution to DCP.

Feb 17 (4:10pm) - Qi Feng (USC)

Title: Accelerating Convergence of Stochastic Gradient MCMC: algorithm and theory

Abstract: Stochastic Gradient Langevin Dynamics (SGLD) shows its advantages in multi-modal sampling and non-convex optimization, which implies broad application in machine learning, e.g. uncertainty quantification for AI safety problems, etc. The core issue in this field is about the acceleration of the SGLD algorithm and convergence rate of the continuous time Langevin diffusion process to its invariant distribution. In this talk, I will present the general idea of entropy dissipation and convergence rate analysis. In the first part, I will show stochastic gradient MCMC algorithms based on replica exchange Langevin dynamics, and empirical studies of our algorithm on optimization and uncertainty estimates for synthetic experiments and image data. In the second part, I will talk about the convergence rate analysis of reversible/non-reversible degenerate Langevin dynamics (i.e. variable coefficient underdamped Langevin dynamics). The talk is based on a series of joint works with W. Deng, L. Gao, G. Karagiannis, W. Li, F. Liang, and G. Lin.

Feb 24 (4:10pm) - Ananda Weerasinghe (ISU)

Title: A diffusion control problem with a path-dependent cost functional

Abstract: We analyze and obtain the solution to a diffusion control problem (DCP) with a cost structure consists of two types of costs: a cost due to increase of the running maximum of the state process and a control cost where the control term effects the linear drift of the state process and it helps to slow down the growth of the running maximum. This DCP is obtained as the diffusion approximation of an infinite-server controlled stochastic processing system. The solution to this DCP enables us to find nearly optimal controls for the control problem associated with the processing system.

Mar 3 (4:10pm) - Nicole Buczkowski (University of Nebraska-Lincoln)

Title: Stability of solutions with respect to changes in data and parameters of nonlocal models

Abstract: Mathematical models that are physically relevant must guarantee existence and uniqueness of solutions, as well as continuity with respect to the data. Thus, small changes in data or parameters will lead to appropriate changes in the solution. These continuity properties are also relevant in numerical analysis, where small variations that are beyond computer precision should not be reflected in the numerical solutions produced. Nonlocal models, with their capability to handle discontinuities, record long range interactions through a kernel which gives additional flexibility. By varying data given by external forces, boundary conditions, the kernel, or the type of nonlinearity used in the model the solution changes accordingly. These changes are quantified through stability or continuity results, which we obtained in linear and nonlinear settings.

Mar 17 (4:10pm) - Kyle Liss (University of Maryland)

Title: Quantitative hypoellipticity and exponential convergence to equilibrium for a class of SDEs

Abstract: Various physical systems (fluids, nonlinear waves, etc.) exhibit turbulent, generally chaotic behavior when subject to forcing and weak damping. In the study of such systems, two important problems once unique ergodicity has been established are to understand properties of the invariant measure and quantify the convergence of generic time averages to the unique stationary statistics. In this talk, we discuss recent work on the long-time behavior of a class of hypoelliptic, weakly damped SDEs that covers some prototypical chaotic/turbulent systems such as Lorenz-96 and Galerkin truncations of the stochastic Navier-Stokes equations. Our main results are an optimal, quantitative estimate on the exponential convergence to equilibrium in the limit of vanishing and balanced noise and dissipation, and uniform (in the small noise/dissipation parameter) pointwise estimates on the stationary density. Exponential convergence for the model under consideration has been known for some time, but our quantitative estimates are new. Our proof uses a scheme that combines a weak Poincaré inequality argument with quantitative hypoelliptic regularization for the associated time-dependent Kolmogorov equation. The uniform estimates on the stationary density are crucial to carrying out our approach, and are proven with hypoelliptic De Giorgi and Moser type iterations. This is joint work with Jacob Bedrossian (University of Maryland).

Mar 24 (4:15pm) - Xin Liu (Clemson University)

Title: Allocation control for Bipartite Matching Queues

Abstract: Motivated by the study of organ transplant systems, we study a multi-class bipartite matching system with one side labeled “patients” and the other side labeled “organs”. We assume that patients may die/delist or move between classes due to changes of their health status. We introduce two stochastic queueing control problems (QCPs) with the same control process that governs the allocation of each arriving organ. For the first QCP, its objective is to maximize the expected life years of all population in the system during a finite time horizon. The second QCP is to minimize a long run average linear holding cost. For both QCPs, we first construct proper deterministic control problems, referred to as the fluid control problems (FCPs) and show that the FCPs provide the best achievable performance for the corresponding QCPs. We next develop an asymptotic framework, in which large-scale matching systems are considered, and show that the fluid scaled QCP attains the corresponding FCP optimal value asymptotically. We propose a simple priority type policy for the first QCP and a randomized allocation policy for the second QCP based on the optimal solutions of the corresponding FCPs. Finally, we establish the asymptotic optimality of the proposed policies through scaling limit theorems.

Mar 31 (4:10pm) - Hayley Olson (University of Nebraska-Lincoln)

Title: Tempered and Truncated Fractional Operators - Exploring Reduction of Computational Costs

Abstract: Fractional calculus operators have been around nearly as long as the familiar integer-order derivatives. The introduction of an exponential tempering to the fractional operator has recently found applications in geophysics and finance. However, the infinite radius of interaction of the operator makes it computationally hefty. In this talk, we explore the use of a truncated fractional operator -- restricted to a bounded domain of integration -- in order to capture the same action of the tempered operator but with an easier computational load.

Apr 7 (4:10pm) - Cecilia Mondaini (Drexel University)

Title: Rates of convergence to statistical equilibrium: a general approach and applications

Abstract: Randomness is an intrinsic part of many physical systems. For example, it might appear due to uncertainty in the initial data, or in the derivation of the mathematical model, or also in observational measurements. In this talk, we focus on the study of convergence/mixing rates for stochastic/random dynamical systems towards statistical equilibrium. Our approach uses the weak Harris theorem combined with a generalized coupling technique to obtain such rates for infinite-dimensional stochastic systems in a suitable Wasserstein distance. We emphasize the importance of obtaining these results via algorithms that are well-defined in infinite dimensions. This allows to obtain convergence rates that are robust with respect to finite-dimensional approximations, thus beating the curse of dimensionality. In particular, we show two scenarios where this approach is applied in the context of stochastic fluid flows. First, to show that Markov kernels constructed from a suitable numerical discretization of the 2D stochastic Navier-Stokes equations converge towards the invariant measure of the continuous system. Second, to approximate the posterior measure obtained via a Bayesian approach to inverse PDE problems, particularly when applied to advection-diffusion type PDEs. Here we present an alternative proof of mixing rates for the exact preconditioned Hamiltonian Monte Carlo algorithm in infinite dimensions, a result that was still an open problem until quite recently. This talk is based on joint works with Nathan Glatt-Holtz (Tulane U).

Apr 14 (4:10pm) - Chao Zhu (University of Wisconsin-Milwaukee)

Title: Regime-Switching Jump-Diffusion Processes with Countable Regimes

Abstract: This work focuses on a class of regime-switching jump diffusion processes, which is a two component Markov processes $(X(t),\Lambda(t))$, where the analog component $X(t) \in R^{d}$ models the state of interest while the switching component $\Lambda(t)\in \{1,2,\dots\}$ can be used to describe the structural changes of the state or random factors that are not represented by the usual jump diffusion formulation. Considering the corresponding stochastic differential equations, our focus is on treating those with non-Lipschitz coefficients. We first show that there exists a unique strong solution to the corresponding stochastic differential equation. Then Feller and strong Feller properties and exponential ergodicity are investigated. This is a joint work with Khwanchai Kunwai, Fubao Xi and George Yin.

Apr 21 (4:10pm) - David Herzog (ISU)

Title: A functional law of the iterated logarithm for weakly hypoelliptic diffusions

Abstract: This is part one of 2-3 talks which will be continued in the fall. In this part, we discuss the method of stochastic characteristics to help motivate laws of the iterated logarithm. This method is often used to solve second-order linear boundary-valued PDEs, such as the Dirichlet or Poisson Problem in a bounded domain in R^n. If the operator L defining the equation is uniformly elliptic in a neighborhood of the domain and the boundary is smooth, then we can always use this method with ease to find an expression for the unique classical solution, provided the data is smooth enough. However, if we drop the ellipticity assumption, say L is only hypoelliptic or "weakly" hypoelliptic (i.e. the randomness is degenerate), then the candidate expression typically makes sense, but it may no longer satisfy the equation. The problem moving from the elliptic to hypoelliptic settings is that the natural elliptic approximation of L may not approximate the defining stochastic characteristics near the boundary, even though the processes defined by these operators are close in a very strong sense. In order to determine when it does approximate L near the boundary, we establish laws of the iterated logarithm for the diffusion at time zero.

Apr 28 (4:10pm) - Sungchan Park (ISU)

Title: Adjusted Linear Preferential Attachment Model with Covariates and Existence of MLE

Abstract: Barabási–Albert (BA) model, or Linear Preferential Attachment Model is an appealing mechanism for modeling a growing network with the property that the more connected a node is, the more likely it is to receive new links. Despite its simplicity and useful results explaining the power-law degree distributions and the first mover advantage, BA model highly underestimates the possibility that late comers may beat the first movers. So, Bianconi-Barabási (BB) model introduced the concept of fitness, an intrinsic and quantitative measure of each node's ability to stay in front of the competition. On the other hand, however, BB model does not offer any idea where the intrinsic ability comes from. We introduced a new model with covariates and parameters that assesses how attributes of each node affect preferential attachment. In particular, we showed the unique MLE exists for the univariate case and found a sufficient condition that the unique MLE exists for multivariate cases. We also explored numerical examples of the model and its MLE.

May 5 (4:10pm) - Nathan Glatt-Holtz (Tulane University)

Title: A Bayesian Approach to Quantifying Uncertainty in Divergence Free Flows.

Abstract: We treat a statistical regularization of the ill-posed inverse problem of estimating a divergence free flow field $u$ from the partial and noisy observation of a passive scalar $\theta$ which is advected by $u$. Our solution is a Bayesian posterior distribution, that is a probability measure $\mu$ of the space of divergence free flow fields which precisely quantifies uncertainties in $u$ once one species models for measurement error and a prior knowledge for $u$.

In this talk we survey some of our recent work which analyzes $\mu$ both analytically and numerically. In particular we discuss a posterior contraction (consistency) result as well as some Markov Chain Monte Carlo (MCMC) algorithms which we have developed, refined and rigorously analyzed to effectively sample from $\mu$. This is joint work with Jeff Borggaard, Justin Krometis and Cecilia Mondaini.