The Bayesian Inference in Stochastic Processes workshop will provide an opportunity to review, discuss and explore directions of development of Bayesian Inference in Stochastic Processes and in the use of Stochastic Processes for Bayesian Inference.
The Workshop will encourage discussion and promote further research in these fields. Theoretical and applied contributions are both welcome. A non-exhaustive list of topics includes Markov processes, state-space models, spatial, empirical, birth-death and branching processes, queueing, population modelling, signal processes, stochastic differential equations. The Workshop will thus be of interest to researchers and workers in both Bayesian Inference and Stochastic Processes.
The Workshop follows the ones held in Madrid in 1998, in Varenna in 2001, La Manga in 2003, Varenna in 2005, Valencia in 2007, Bressanone in 2009, Madrid in 2011, Milano in 2013, Istanbul in 2015, Milano in 2017 and Madrid in 2019.
Ten presentations by young scientists have been scheduled this year, followed by an in-depth discussion by senior scholars in the field
Approximate Bayesian Conditional Copulas
Discussant: Maria Concepcion Ausin, Universidad Carlos III, Spain
Bayesian Dynamic Fused LASSO
Discussant: Sylvia Frühwirth-Schnatter, Vienna University of Economics and Business, Austria
Multiscale Bayesian Survival Analysis
Discussant: Antonio Lijoi, Università Bocconi, Italy
Bayesian Modeling of Power Outages and Their Consequences
Discussant: Melike Baykal-Gursoy, Rutgers University, USA
From viral evolution to spatial contagion: a biologically modulated Hawkes model
Discussant: Katja Ickstadt, Technical University Dortmund, Germany
Bayesian Network Inference With Uncertain Evidence and Parameters
Discussant: David Banks, Duke University, USA
Bayesian forecasting dynamic models under attacks
Discussant: Mike West, Duke University, USA
Quasi-stationary Monte Carlo methods via stochastic approximation
Discussant: Giacomo Zanella, Università Bocconi, Italy
Compositions of discrete random probabilities for inference on multiple samples
Discussant: Bernardo Nipoti, Università di Milano-Bicocca, Italy
Bayesian Conditional Auto-Regressive LASSO Models to Learn Sparse Networks with Predictors
Discussant: Nicholas Polson, University of Chicago, USA
BISP12 welcomed the submission of contributed virtual posters. Abstracts were submitted from Register and Submit section where instructions and templates were available.
Posters were presented as pre-recorded video and made available on the BISP12 website.
25 Apr 2021
30 Apr 2021
26 May 2021
but registration is required.
14.00-14.40 Talk 1: Clara Grazian
14.45-15.25 Talk 2: Kaoru Irie
15.30-16.10 Talk 3: Stéphanie Van Der Pas
16.15-16.55 Talk 4: Atilla Ay
17.00-17.40 Talk 5: Andrew Holbrook
14.00-14.40 Talk 6: Paul Wu
14.45-15.25 Talk 7: Roi Naveiro
15.45-16.25 Talk 8: Andi Wang
16.30-17.10 Talk 9: Giovanni Rebaudo
17.15-17.55 Talk 10: Claudia Solis-Lemus