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Seminar announcement
by Marco Scarsini 19 Feb '21

19 Feb '21

Thursday, 25 February, 17:00 CET *Daniela Saban,* Stanford University Online Assortment Optimization for Two-sided Matching Platforms <http://mailsender.luiss.it/lists/lt.php?id=cRpQAAwDSAUFBQBOVgFdBQs> Discussants: Will Ma and Antoine Désir The paper can be found here <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3712553>. This is the link to the Virtual Room <http://mailsender.luiss.it/lists/lt.php?id=cRpQAAYBSAUFBQBOVgFdBQs> The schedule of the upcoming seminars can be found here <https://economiaefinanza.luiss.it/en/seminari/prossimi-seminari>. If you want to receive announcements of future seminars, please fill out this form <https://docs.google.com/forms/d/e/1FAIpQLSeDxM-M5ogG43elgjC-lNCp0BDhISiZHG0…> . ******************************************************* Marco Scarsini Dipartimento di Economia e Finanza Luiss Viale Romania 32 00197 Roma, ITALY URL: http://docenti.luiss.it/scarsini/

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Padova Online Seminar by Luca Avena (Leiden University) [Friday, Feb. 26 at 14.30]
by alessandra bianchi 19 Feb '21

19 Feb '21

Dear Colleagues, We would like to invite you to the following Probability seminar that will take place on February 26 at 14.30 by the zoom platform. ________________________________________________________ Speaker: Luca Avena (Leiden University) Title: Node immunization in networks: a scalable searching algorithm based on random rooted forests 26 FEBRUARY (Friday) - 14:30 zoom link: TBA available on the webpage https://www.math.unipd.it/~bianchi/seminari/ ) Abstract: We are interested in the so-called multiple-node immunization for complex networks under attack of a viral agent. The latter is a hot topic in network science and it consists of identifying and removing a set of nodes of a given size in a graph to maximally impede the agent spread. Several approaches have been proposed in the literature based on numerical and theoretical insights on how classical models for virus spread (so-called compartmental models) evolve on graphs. Based on the stability analysis of these compartmental models, the maximal eigenvalue of the adjacency matrix of the graph has been proposed as a measure for how resilient the network is. Thus one of the most common approaches for immunization consists in identifying the set of nodes of a given cardinality, for which the reduced network, obtained by removing these nodes, has minimal largest eigenvalue. This optimization problem turns out to be a computationally hard problem in the well known NP class and the available exact or proxy algorithms offer good solutions and performances only for small data sets. We propose a novel randomized flexible method to efficiently identify these sets of nodes based on random walk kernels and random rooted forests. We explain the theoretical results underlying the method, and present experimental results where we test method and performances on classical synthetic and real-world benchmarks. Joint work with Michael Emmerich, Alex Gaudilliere and Irina Gurewitsch -- Alessandra Bianchi Dip. di Matematica Università di Padova Via Trieste, 63 - 35121 Padova, Italy phone: +39 049 827 14 06 fax: +39 049 827 14 28 e-mail: bianchi(a)math.unipd.it http://www.math.unipd.it/~bianchi/

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Webinar in Statistics YUN WEI - 26/2, ore 17
by Pierpaolo De Blasi 19 Feb '21

19 Feb '21

WEBINARS IN STATISTICS @ COLLEGIO CARLO ALBERTO <https://www.carloalberto.org/events/category/seminars/seminars-in-statistic…> Venerdi 26 Febbraio 2021, alle ore 17:00, si terrà il seguente webinar: ------------------------------------------------ Speaker: *Yun Wei (*Samsi and Duke University, USA) Title: *Obtaining faster convergence rates in finite mixture models by taking repeated measures* Zoom link: https://us02web.zoom.us/j/89954920036?pwd=dndsQnZqQ2crZzVlRW1pM0Q2RGo1Zz09 Meeting ID: 899 5492 0036 Passcode: 418668 Abstract: It is known that some finite mixture models suffer from slow rates for estimating the component parameters. Examples are mixtures of the weakly identifiable families in the sense of [Ho and Nguyen 2016]. To obtain faster parameter convergence rates, we propose to collect more samples from each mixture component, hence each data is a vector of samples from the same mixture component. Such a model is known in the literature as a finite mixture model of repeated measures, which has been applied in psychological study and topic modeling. This model also belongs to the mixture of product distributions, with the special structure that the product distributions in each mixture component are also identical. In this setup, each data consists of conditionally independent and identically distributed samples and thus is an exchangeable sequence. We show that by taking repeated measures (collecting more samples from each mixture component), a finite mixture model that is not originally identifiable becomes identifiable. Moreover, the posterior contraction rates for the parameter estimation are also obtained, demonstrating that repeated measures are beneficial for estimating the component parameters. Our results hold for general probability families including all regular exponential families and can also be applied to hierarchical models. The key tool to develop the results is by establishing an inverse inequality to upper bound a suitable distance between mixing measures by the total variational distance between the corresponding mixture densities. Based on joint work with Xuanlong Nguyen (University of Michigan). ------------------------------------------------ Il webinar è organizzato dalla "de Castro" Statistics Initiative www.carloalberto.org/stats in collaborazione con il Collegio Carlo Alberto e rientra nel Complex Data Modeling Research Network midas.mat.uc.cl/network Cordiali saluti, Pierpaolo De Blasi --- University of Torino & Collegio Carlo Alberto carloalberto.org/pdeblasi <https://sites.google.com/a/carloalberto.org/pdeblasi/>

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Fwd: PhD at the Scuola Normale Superiore (Pisa, Italy). Call 2021-2022
by Fabrizio Lillo 18 Feb '21

18 Feb '21

The call for applications to the 2021/2022 PhD program in *Computational Methods and Mathematical Models for Sciences and Finance* at the Scuola Normale Superiore, Pisa, see https://www.sns.it/en/computational-methods-and-mathematical-models-for-sci… ., is available here https://www.sns.it/en/admissions/phd/how-to-apply-for-the-phd-courses Please distribute this information to potentially interested candidates. Thank you very much in advance. ---------------------------------------- Fabrizio Lillo Dipartimento di Matematica, Università di Bologna Scuola Normale Superiore, Pisa ITALY Personal website: fabriziolillo.wordpress.com University website: www.unibo.it/sitoweb/fabrizio.lillo <http://fabriziolillo.wordpress.com/> phone: +39 050509159

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Fwd: [owps] One World Probability Seminar Thursday February 18, 2021
by Faggionato, Alessandra 17 Feb '21

17 Feb '21

Buongiorno, inoltro l'annuncio del OWPS di domani. Saluti Alessandra ---------- Forwarded message --------- Da: One World Probability <ow.probability(a)gmail.com> Date: mer 17 feb 2021 alle ore 10:50 Subject: [owps] One World Probability Seminar Thursday February 18, 2021 To: <owps(a)lists.bath.ac.uk> Tomorrow's speaker in the One World Probability Seminar is (Note: all times are in UTC. *Due to time changes, you should check what that translates to in your location*) ------------------------------------------------ (14:00-16:00 UTC) Speaker: Richard Kenyon (Yale) Title: The multi-tiling model Abstract: Given a graph G and collection of connected subgraphs T (called tiles), we consider covering G with copies of tiles in T so that each vertex of G is covered with a predetermined multiplicity. The multitiling model is a natural probability measure on such configurations. In the limit of large multiplicities we compute the asymptotic growth rate of the number of multitilings: the free energy of the multitiling model. We will show that the individual tile densities tend to a Gaussian field with respect to an associated discrete Laplacian. We also find an exact discrete Coulomb gas limit when we vary the multiplicities. This is joint work with Andrei Pohoata (Yale). Please note: Richard Kenyon will be giving both talks in the session. ------------------------------------------------ The zoom link will appear the day before on the OWPS website: https://www.owprobability.org/one-world-probability-seminar <https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.owpro…> It can also be directly accessed through the link below: https://uniroma1.zoom.us/j/83228198137?pwd=QUxhM1NXdlFTOGxvc09IUGIvenBxUT09 <https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Funiroma1.…> Meeting-ID: 832 2819 8137 Passcode: 029896 Please feel free to circulate this email. We hope to see you all tomorrow! One World Probability Team -- ************************************************* Prof. Alessandra Faggionato http://www1.mat.uniroma1.it/~faggionato/ Department of Mathematics University "La Sapienza" Piazzale Aldo Moro, 5 00185 - Rome Office 5, Phone (0039) 06 49913252 *************************************************

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Workshop "Bayesian Inference in Stochastic Processes - Online", 27-28 May 2021
by elisa varini 17 Feb '21

17 Feb '21

On behalf of the Scientific and Organising Committees we are pleased to inform you that the Twelth Workshop on “*Bayesian Inference in Stochastic Processes (BISP12)*” will be held *online* on *27-28 May 2021* (afternoons, Central European Summer Time). Attendance is free of charge but registration is required (and limited to 150 people). As in the past, the workshop will provide an opportunity to review, discuss and explore directions of development of Bayesian Inference in Stochastic Processes. Ten presentations by young scientists have been scheduled this year, followed by an in-depth discussion by senior scholars in the field. The workshop is organised by CNR-IMATI (Milano), www.imati.cnr.it. Information on programme, website and registration will be available in early Spring. Elisa Varini and Fabrizio Ruggeri Chairs, Organising and Scientific Committees

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Pisa Online Seminars - Renaud Raquépas (Tuesday, Feb. 23 at 16:00)
by Giacomo Filippo Di Gesu 16 Feb '21

16 Feb '21

Dear colleagues, I would like to invite you to the following online seminar organized by the Probability group of the University of Pisa. The talk will be accessible under the link Click here to join the meeting<https://teams.microsoft.com/l/meetup-join/19%3A17115d7f6ef44c5e91974362906c…> Best regards, Giacomo Tuesday, Feb. 23, 16:00 Speaker: Renaud Raquépas (McGill University Montréal and Université Grenoble Alpes) Title: Entropy production in nondegenerate diffusions: the large-time and small-noise limits Abstract: Entropy production (EP) is a key quantity originating from thermodynamics and statistical physics which quantifies the irreversibility of the time evolution of physical systems. I will start with a general introduction to the different approaches to defining EP. Then, I will focus on the context of nondegenerate diffusions and I will describe the large-deviation properties of EP as time goes to infinity. Finally, I will discuss the behaviour of the corresponding rate function as the intensity of the noise goes to zero. ************************ Giacomo Di Gesù Dipartimento di Matematica Università di Pisa Largo Bruno Pontecorvo 5 56127 - Pisa, Italy giacomo.digesu(a)unipi.it<mailto:giacomo.digesu@unipi.it> https://sites.google.com/site/giacomodigesu/

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Sussex probability seminar
by Enrico Scalas 15 Feb '21

15 Feb '21

Dear Colleagues, On Monday 22nd February at 11am UK time, Minmin Wang (Department of Mathematics, University of Sussex) will present a seminar on k-cut model for the Brownian Continuum Random Tree Paper: https://arxiv.org/abs/2007.11080 If you are interested in joining the seminar, please contact Dr Wang at: minmin.wang(a)sussex.ac.uk. Best regards, Enrico Scalas

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Fwd: Funded PhD positions at University College Dublin
by Bisi, Elia 12 Feb '21

12 Feb '21

Dear colleagues, Applications are open for PhD scholarships in Maths & Stats at University College Dublin — see announcement below. Possible projects in probability will be supervised by Neil O’Connell. Cheers, Elia Bisi ————————— Elia Bisi Assistant Professor (non-TT) Vienna University of Technology (TU Wien) Research Unit Mathematical Stochastics https://eliabisi.com ---------- Forwarded message ---------- From: Neil O'Connell <neil.oconnell(a)ucd.ie> Date: 12 Feb 2021, 16:46 +0100 To: Neil O'Connell <neil.oconnell(a)ucd.ie> Subject: Funded PhD positions at University College Dublin Dear colleagues, I would be grateful if you could forward the announcement below to any suitable mailing lists or students who might be interested: Applications are invited for the UCD School of Mathematics and Statistics Research Demonstratorship scholarship scheme. These scholarships fund up to 4 years full-time PhD research, beginning on 1st September 2021. The closing date for applications is March 9th 2021 with results anticipated by early April 2021. Details on the scholarships and the application process are available at www.ucd.ie/mathstat/study/researchdegrees/rd2021<http://www.ucd.ie/mathstat/study/researchdegrees/rd2021>. Best regards, Neil O'Connell

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Gubinelli graduate course
by Stefania Ugolini 12 Feb '21

12 Feb '21

Università degli Studi di Milano: PhD program Stochastic quantization of the Euclidean quantum field theory Lecturer: Prof. Dr. Massimiliano Gubinelli The goal of Euclidean quantum field theory is to build probability measures on the space of distributions satisfying properties such as Euclidean invariance, reflection positivity and non-triviality, that allows to recover an interacting relativistic quantum field satisfying Wightman axioms. Stochastic quantization, first proposed by Parisi–Wu and Nelson, is a method of construction of such measures via stationary solutions of a stochastic partial differential equations driven by additive Gaussian white noise. In this course we will learn about the stochastic quantization of the Euclidean quantum field theory of a scalar boson with quartic interaction and its main properties. We introduce the Φ43 measure as the limit of the invariant measure of a finite dimensional system of stochastic differential equations. The proof proposed uses several analytic and probabilistic techniques, such as white noise analysis, weighted Besov spaces on lattice and paraproducts, which also find applications in other problems arising in the study of deterministic and stochastic singular differential equations. All these tools and ideas will be gradually introduced and explained during the lectures. The course is as much as possible self-contained and requires as a prerequisite only basic knowledge of stochastic and functional analysis. Scheduling: February 15, 16, 18, 22, 25 from 10:00 to 12:00 and from 14:00 to 16:00 Topic: Lectures on Stochastic Quantisation Time: This is a recurring meeting Meet anytime Join Zoom Meeting https://www.iam.uni-bonn.de/abteilung-gubinelli/sq-lectures-milan-ws2021(ht…" target="_blank">https://uni-bonn.zoom.us/j/94642762037?pwd=MG80cW92T29PMWw3dDQ3aEdsY2lsdz09 Meeting ID: 946 4276 2037 Passcode: 954150 The link is available also at the following page: Course page: <signaturebeforequotedtext></signaturebeforequotedtext><signatureafterquotedtext></signatureafterquotedtext>

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