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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

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 formulationfitting 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.

Programme

 

Program (can be changed)

 

 

Sunday 1

Monday 2

Tuesday 3

Wednesday 4

 8h30-10h00

 

Speaker

Speaker

Speaker

10h00-10h30

 

Coffee Break

Coffee Break

Coffee Break

10h30-12h00

 

Speaker

Speaker

Speaker

12h00-14h00

 

Lunch

Lunch

lunch

14h00-15h00

 

 

 Comité scientifique

 

15h30-17h00

Welcome tea

Coffee Break

Coffee Break

 

17h00-18h30

Speaker

Speaker

Speaker

18h30-19h30

Apero

 

 

19h30-21h00

Dinner

Dinner

Dinner

 

 

 

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
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