Program
Saturday, November 28
Welcome |
Session I | Chair: Sylvia Frühwirth-Schnatter (WU Vienna) | |
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905-950 | Florian Huber (University of Salzburg) Forecasting with Bayesian Non-Parametric Vector Autoregressive Models |
950-1000 | Short break |
1000-1025 | Florian Frommlet (Medical University of Vienna) A Genetically Modified Mode Jumping MCMC Algorithm for Model Selection in Non-Standard Applications. |
1025-1050 | Jan Greve (WU Vienna) Spying on the prior of the number of clusters and partition distributions in Bayesian cluster analysis |
1050-1115 | Markus Hainy (JKU Linz) Bayesian Experimental Design for Models with Intractable Likelihoods via Supervised-Learning Methods |
1115-1130 | Short break |
Session II | Florian Huber (University of Salzburg) | |
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1130-1155 | Niko Hauzenberger (University of Salzburg) Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods |
1155-1220 | Paul Hofmarcher (University of Salzburg) Bayesian Model Averaging: Review und Perspectives |
1220-1245 | Darjus Hosszejni (WU Vienna) The Role of the stochvol Package in Bayesian Dynamic Covariance Estimation |
1245-1345 | Lunch |
Session III | Chair: Bettina Grün (WU Vienna) | |
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1345-1430 | Helga Wagner (JKU Linz) Bayesian modelling of treatment effects |
1430-1440 | Short break |
1440-1505 | Gregor Kastner (University of Klagenfurt) Time-varying Risk Premia and Volatility Dynamics in Multi-Asset Class Returns. |
1505-1530 | Peter Knaus (WU Vienna) The triple gamma – A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models |
1530-1555 | Gertraud Malsiner-Walli (WU Vienna) Generalized mixtures of finite mixtures |
1555-1610 | Short break |
Session IV | Chair: Helga Wagner (JKU Linz) | |
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1610-1635 | Nikolaus Umlauf (University of Innsbruck) Scalable Distributional Regression |
1635-1700 | Laura Vana (WU Vienna) Verification of spatio-temporal properties in a Bayesian model using signal spatio-temporal logic |
1700-1725 | Gregor Zens (WU Vienna) Ultimate Pólya Gamma Samplers – Efficient MCMC for Possibly Imbalanced Binary and Categorical Data |
1725 | Closing |