Information détaillée concernant le cours
Titre | École d’été 2024 |
|||||||||||||||||||||||||||||||||||||||||||||||
Dates | 1-4 septembre 2024 |
|||||||||||||||||||||||||||||||||||||||||||||||
Responsable de l'activité | Christian Mazza |
|||||||||||||||||||||||||||||||||||||||||||||||
Organisateur(s)/trice(s) | Prof. Christian Mazza, Université de Fribourg Mme Caroline Gillardin, coordinatrice CUSO Co organisation avec l'EPFL
|
|||||||||||||||||||||||||||||||||||||||||||||||
Intervenant-e-s | Prof. Bartek Blaszczyszyn, INRIA, Paris, France Prof. Roland Langrock, Bielefeld University, Allemagne Prof. Antonietta Mira, Università della Svizzera italiana, Suisse
|
|||||||||||||||||||||||||||||||||||||||||||||||
Description | Prof. Bartek Blaszczyszyn, INRIA, Paris, France
Title : Ergodic Learning of Spatial Geometric Structures
Abstract: Ergodicity serves as a crucial link between probability theory and statistics. In spatial statistics, it connects various spatial averages to their corresponding mathematical expectations. A remarkable—and perhaps underemphasized—implication of ergodicity is that, in theory, a single complete realization of a stationary ergodic model almost surely allows one to estimate the underlying distribution of the model.
In the first lesson, we will revisit these foundational results in the context of point processes. The second lesson will explore how ergodicity can be leveraged to develop generative models for point processes, learned from a single realization. In the final lesson, we will narrow our focus to learning some striking feature of the model, namely hyperuniformity, and provide mathematical limiting results that justify this approach to ergodic learning.
**Bibliography for the course:** 1. Blaszczyszyn, B. *Lecture Notes on Random Geometric Models*; hal:cel-01654766. 2. Brochard, A., Blaszczyszyn, B., Mallat, S., and Zhang, S. (2022). *Particle Gradient Descent Model for Point Process Generation*. *Statistics and Computing*; arXiv:2010.14928. 3. Mastrilli, G., Blaszczyszyn, B., Lavancier, F. (2024). *Estimating the Hyperuniformity Exponent of Point Processes*. arXiv:2407.16797.
Prof. Roland Langrock, Bielefeld University, Allemagne
Title : Hidden Markov models Abstact : Hidden Markov models (HMMs) are flexible statistical models for sequences of observations that are driven by underlying states. Over the last two decades, this class of models has become increasingly popular in applied statistics since many real-world phenomena naturally translate to the HMM framework: for example, observed animal movement depends on not directly observed behavioural modes, financial share returns depend on the underlying market volatility, and medical measurements depend on the patient's underlying health state. In such scenarios, HMMs allow for comprehensive statistical inference, including forecasting, state decoding and investigations of the system's response to internal and external drivers.This mini-course will introduce the HMM framework, covering the following topics: – overview & basic model formulation– fitting an HMM to data – model selection & model checking – state decoding – incorporating covariates, random effects and seasonality – extensions of the basic model formulation
Prof. Antonietta Mira, Università della Svizzera italiana, Suisse
Title : How can Bayesian statistics help in dimensionality reduction?
Abstract : I will introduce the Bayesian paradigm to statistical inference, and then explain how it can be exploited to estimate the intrinsic dimension (ID) of data, and to answer questions related to dimensionality reduction that are becoming more pressing as the size of available data becomes larger. Indeed, real-world datasets tend to show a high degree of (possibly) non-linear correlations and constraints between their features. This means that, despite a very large embedding dimensionality, data typically lie on a manifold characterized by a much lower ID. which, in the presence of noise, may depend on the scale at which the data is analysed. This fact rises interesting questions: How many variables, or combinations there of, are necessary to describe a real-world data set without significant information loss? What is the appropriate scale at which one should analyze and visualize the data? These two issues, which are often considered unrelated, are actually strongly entangled, and can be addressed within a unified framework. We introduce an approach in which the optimal number of variables and the optimal scale are determined self-consistently, recognizing and bypassing the scale at which the data are affected by noise. To this aim we estimate the data ID in an adaptive way, and exploit it as a summary statistics in Approximate Bayesian Computation for inference in network type data. Sometimes, within the same dataset, it is possible to identify more than one ID meaning that different subsets of the data points lie onto manifolds with different IDs. Identifying these manifold provides a clustering of the data, and in many real world applications a simple topological feature, like the ID, allows to uncover a rich data structure and improves our insight into subsequent statistical analysis. Examples of these applications range from gene expression to protein folding, pandemic evolution, FMRI, all the way to finance, sport data and the analysis of the representations of neural networks.
slides 1 and slides 2 for the Bayesian part of the lectures
https://youtu.be/UTjmU8TRnQE?si=gP_TlucKln6BdhHw
https://www.amstat.org/asa/files/pdfs/p-valuestatement.pdf
|
|||||||||||||||||||||||||||||||||||||||||||||||
Programme |
Program (can be changed)
Conférences and coffee breaks in : Hôtel des Masques (2 mns walk from Hôtel Eden), Place du Village 7, 1972 Anzère
Wecome tea Sunday : Hôtel Eden
Apero Sunday : Bar Hôtel Eden
Breakfast, lunch and dinner : Hôtel Eden
|
|||||||||||||||||||||||||||||||||||||||||||||||
Lieu |
Anzère - Eden Resort - Alpes valaisannes |
|||||||||||||||||||||||||||||||||||||||||||||||
Information | Hôtel : Eden Resort Anzère
Adress : Route d'Anzère 34, 1972 Anzère, Canton du Valais
Tel +41 (0)27 399 31 00 L'hôtel est situé dans les Alpes valaisannes. Il y a des chambres et des suites.
Accès à Anzère en voiture: Google Map
EN AVION Aéroports internationaux de: - Genève (180 km) - Zürich (290 km) - Bâle (270 km)
En transport public Horaires Swiss Train :
De: Aéroport de Genève : Train jusqu'à Sion. A la gare de Sion : car postal Sion - Anzère centre (36 mns)
Swiss Train schedule : From: Geneva airport, To: Sion. From Sion station : postal bus to Anzère centre. Travel time: Genève - Sion (1 hour and 55 minutes); Sion - Anzère (36 minutes).
Visa pour la Suisse (demande de visa suisse en ligne)
Météo en suisse (meteoswiss.admin.ch)
|
|||||||||||||||||||||||||||||||||||||||||||||||
Frais | Tarif :
CUSO (UNINE, UNIGE, UNIL, UNIFR, UNIBE, IHEID, HES-SO)
Doctorant-e CUSO chambre double: 200 CHF
Doctorant-e CUSO chambre simple: 350 CHF
Post-doctorant-e CUSO chambre double: 300 CHF
Post-doctorant-e CUSO chambre simple: 450 CHF
Professeur-e CUSO chambre double: 400 CHF
Professeur-e CUSO chambre simple: 550 CHF
Non CUSO privé-e chambre double: 1300 CHF
Non CUSO privé-e chambre simple: 1500 CHF
Lors de votre inscription, merci de bien vouloir indiquer dans la zone commentaire si vous désirez une chambre simple, ou double et le nom de la personne avec qui vous souhaiteriez partager votre chambre. Dans le cas où rien n'est indiqué, une chambre simple sera réservée. Si vous vous inscrivez et que vous ne pouvez pas participer, veuillez nous contacter dès que possible. Dans le cas contraire, toutes les nuits d'hôtel pourraient vous être facturées.
*Condition d'annulation :
Pour une annulation effectuée entre la date de réservation et 30 jours avant l'arrivée aucun frais d'annulation ne sera prélevé.Pour une annulation effectuée entre 7 et 30 jours avant l'arrivée un montant équivalent à 30 % de la réservation sera prélevé.Pour une annulation effectuée entre 7 jours avant l'arrivée et le jour de l'arrivée la Totalité de la réservation sera prélevé. Pour une annulation effectuée entre le jour de l'arrivée et le no show la Totalité de la réservation sera prélevé. Pour cause d'accident ou maladie, etc, merci de nous livrer un certificat médical.
*Cancellation policy: For cancellations made between the reservation date and 30 days before arrival, no cancellation fee will be charged. For cancellations made between 7 and 30 days before arrival, an amount equivalent to 30% of the reservation will be charged. For cancellations made between 7 days and the day of arrival, the full amount of the reservation will be charged. For cancellations made between the day of arrival and no-show, the full amount of the reservation will be charged. In the event of accident or illness, etc, please provide a medical certificate.
|
|||||||||||||||||||||||||||||||||||||||||||||||
Inscription | Versement sur compte postal (payment into postal account) :
CUSO CCP 12-1873-8 Neuchâtel BIC : POFICHBEXXX IBAN : CH0509000000120018738. Merci d'écrire votre nom suivi du no" 24220001 " lors du paiement. Thank you to write your name on the payment wording and nb" 24220001".
|
|||||||||||||||||||||||||||||||||||||||||||||||
Places | 30 |
|||||||||||||||||||||||||||||||||||||||||||||||
Délai d'inscription | 24.08.2024 |
![short-url](/typo3conf/ext/speciality/Resources/Public/Images/icons/short_url.png)
![short-url](/typo3conf/ext/speciality/Resources/Public/Images/icons/short_url.png)