Aki Vehtari

Aki Vehtari

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.

I'm member of development teams of Stan and ArviZ

Full researcher profile

Contact information

Postal address
Konemiehentie 2 02150 Espoo Finland
Phone number

Areas of expertise

Bayesian modeling, Statistical analysis, Epidemiology, Brain signal analysis, Machine learning

Honors and awards

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.
Award or honor granted for a specific work Department of Computer Science Jan 2020

Member of the winning team (Särkkä, Vehtari & Lampinen) in Time Series Prediction Competition - The CATS Benchmark 2004

Invitation or ranking in competition Department of Computer Science 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
Award or honor granted for a specific work Department of Computer Science Jun 2016


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