[ibayesclub.beta] Bayes Club, 21st of May (Friday), 3-5 pm (CEST)

[Szabo, B.T.]

Dear  Colleagues,

The next International Bayes Club (https://www.math.vu.nl/thebayesclub/) meeting will take place on the 21st of May (Friday) from 3 pm (CEST). We have two speakers: Matteo Giordano  (Cambridge) and Matthew Stephens (Chicago). Please find below the titles and the abstracts. After the talks we will open up our  virtual meeting place at gather.town so if you want to hang out and perhaps have a beer virtually with your colleagues then you are invited to join. The seminar will be on zoom:

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Topic: International Bayes club
Time: May 21, 2021 03:00 PM Amsterdam, Berlin, Rome, Stockholm, Vienna

Join Zoom Meeting
https://vu-live.zoom.us/j/95319105586?pwd=MXlIWXIvNjcwQ0k1YzErMlkyTzNZQT09

Meeting ID: 953 1910 5586
Passcode: 645009
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Link to the meeting place at gather town:

https://gather.town/app/4lK8Zj28Ys0WtutP/IBC
Password: bayesbayesbayes

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3 pm Speaker: Matteo Giordano (Cambridge)

Title: Nonparametric Bayesian inference for reversible multi-dimensional
diffusions

Abstract: The talk will present frequentist asymptotic results for
nonparametric Bayesian models of reversible multi-dimensional diffusions
with periodic drift. Assuming continuous observation paths,
reversibility is exploited to prove a general posterior contraction rate
theorem for the drift gradient vector field under
approximation-theoretic conditions on the induced prior for the
invariant measure. The general theorem is applied to Gaussian priors and
p-exponential priors, which are shown to converge to the truth at the
minimax optimal rate over Sobolev smoothness classes in any dimension.

Joint work with Kolyan Ray (Imperial College London).

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4 pm Speaker: Matthew Stephens (Chicago)

Title: A simple new approach to variable selection in regression, with
application to genetic fine-mapping

Abstract: We introduce a simple new approach to variable selection in linear
regression, and to quantifying uncertainty in selected variables. The
approach is based on a new model -- the
``Sum of Single Effects'' (SuSiE) model -- which comes from writing
the sparse vector of regression coefficients as a sum of
``single-effect'' vectors, each with one non-zero element. We also
introduce a corresponding new fitting procedure -- Iterative Bayesian
Stepwise Selection (IBSS) -- which is a Bayesian analogue of stepwise
selection methods. IBSS shares the computational simplicity and speed
of traditional stepwise methods, but instead of selecting a single
variable at each step, IBSS computes a {\it distribution} on variables
that captures uncertainty in which variable to select.
The method leads to a convenient, novel, way to summarize uncertainty
in variable selection, and provides a Credible Set for each selected
variable.
Our methods are particularly well suited to settings where variables
are highly correlated and true effects are sparse, both of which are
characteristics of genetic fine-mapping applications.
We demonstrate through numerical experiments that our methods
outperform existing methods for this task.

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