Aki Vehtari
Professor
Professor
T313 Dept. Computer Science
I'm co-leader of the Probabilistic Machine Learning Group at Aalto. We develop new methods for probabilistic modeling, Bayesian inference and machine learning. Our current focuses are in particular probabilistic programming, learning from multiple data sources, Bayesian model assessment and selection, approximate inference and information visualization.
Full researcher profile
https://research.aalto.fi/...
Kontakuppgifter
E-post
[email protected]
Postadress
Konemiehentie 2
02150 Espoo
Finland
Telefonnummer
+358405333747
Kompetensområde
Bayesian modeling, Statistical analysis, Epidemiology, Brain signal analysis, Machine learning
Utmärkelser
Youden Award in Interlaboratory Testing from the American Statistical Association
Youden Award in Interlaboratory Testing from the American Statistical Association awarded to paper Sebastian Weber, Andrew Gelman, Daniel Lee, Michael Betancourt, Aki Vehtari, and Amy Racine-Poon (2018). Bayesian aggregation of average data: An application in drug development. Annals of Applied Statistics, 12(3):1583-1604.
Palkinto tai huomionosoitus tuotoksesta
Tietotekniikan laitos
Jan 2020
Member of the winning team (Särkkä, Vehtari & Lampinen) in Time Series Prediction Competition - The CATS Benchmark 2004
Sijoittuminen kilpailussa tai osallistuminen kutsukilpailuun
Tietotekniikan laitos
Jan 2004
2016 De Groot Prize
The DeGroot Prize, in honor of Morris H ("Morrie" DeGroot, is awarded to the author or authors of an outstanding published book in Statistical Science
Palkinto tai huomionosoitus tuotoksesta
Tietotekniikan laitos
Jun 2016
Publikationer
Detecting and diagnosing prior and likelihood sensitivity with power-scaling
Noa Kallioinen, Topi Paananen, Paul Christian Bürkner, Aki Vehtari
2024
STATISTICS AND COMPUTING
Fast Methods for Posterior Inference of Two-Group Normal-Normal Models
Philip Greengard, Jeremy Hoskins, Charles C. Margossian, Jonah Gabry, Andrew Gelman, Aki Vehtari
2023
Bayesian Analysis
Prior knowledge elicitation: The past, present, and future
Petrus Mikkola, Osvaldo Martin, Suyog Halasinamara Chandramouli, Marcelo Hartmann, Oriol Abril Pla, Owen Thomas, Henri Pesonen, Jukka Corander, Aki Vehtari, Samuel Kaski, Paul-Christian Burkner, Arto Klami
2023
Bayesian Analysis
Bird tolerance to humans in open tropical ecosystems
Peter Mikula, Oldřich Tomášek, Dušan Romportl, Timothy K. Aikins, Jorge E. Avendaño, Bukola D.A. Braimoh-Azaki, Adams Chaskda, Will Cresswell, Susan J. Cunningham, Svein Dale, Gabriela R. Favoretto, Kelvin S. Floyd, Hayley Glover, Tomáš Grim, Dominic A.W. Henry, Tomas Holmern, Martin Hromada, Soladoye B. Iwajomo, Amanda Lilleyman, Flora J. Magige, Rowan O. Martin, Marina F. Marina, Eric D. Nana, Emmanuel Ncube, Henry Ndaimani, Emma Nelson, Johann H. van Niekerk, Carina Pienaar, Augusto J. Piratelli, Penny Pistorius, Anna Radkovic, Chevonne Reynolds, Eivin Røskaft, Griffin K. Shanungu, Paulo R. Siqueira, Tawanda Tarakini, Nattaly Tejeiro-Mahecha, Michelle L. Thompson, Wanyoike Wamiti, Mark Wilson, Donovan R.C. Tye, Nicholas D. Tye, Aki Vehtari, Piotr Tryjanowski, Michael A. Weston, Daniel T. Blumstein, Tomáš Albrecht
2023
Nature Communications
Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity
Martin Modrak, Angie H. Moon, Kim Shinyoung, Paul-Christian Burkner, Niko Huurre, Kateřina Faltejsková, Andrew Gelman, Aki Vehtari
2023
Bayesian Analysis
Using reference models in variable selection
Federico Pavone, Juho Piironen, Paul Christian Bürkner, Aki Vehtari
2023
Computational Statistics
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gabriel Riutort-Mayol, Paul Christian Bürkner, Michael R. Andersen, Arno Solin, Aki Vehtari
2023
STATISTICS AND COMPUTING
An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models
Juho Timonen, Nikolas Siccha, Ben Bales, Harri Lähdesmäki, Aki Vehtari
2023
Stat
Projection Predictive Inference for Generalized Linear and Additive Multilevel Models
Alejandro Catalina, Paul Burkner, Aki Vehtari
2022
INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 151
R* : A Robust MCMC Convergence Diagnostic with Uncertainty Using Decision Tree Classifiers
Ben Lambert, Aki Vehtari
2022
Bayesian Analysis