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/...
Sähköposti
[email protected]
Puhelinnumero
+358405333747
Osaamisalueet
Bayesian modeling, Statistical analysis, Epidemiology, Brain signal analysis, Machine learning
Palkinnot
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
Tutkimusryhmät
- Computer Science Professors, Professor
- Computer Science - Artificial Intelligence and Machine Learning (AIML), Professor
- Probabilistic Machine Learning, Professor
- Professorship Vehtari Aki, Professor
- Helsinki Institute for Information Technology (HIIT), Professor
Julkaisut
Bayesian cross-validation by parallel Markov chain Monte Carlo
Alex Cooper, Aki Vehtari, Catherine Forbes, Dan Simpson, Lauren Kennedy
2024
STATISTICS AND COMPUTING
Active Statistics : Stories, Games, Problems, and Hands-on Demonstrations for Applied Regression and Causal Inference
Andrew Gelman, Aki Vehtari
2024
Detecting and diagnosing prior and likelihood sensitivity with power-scaling
Noa Kallioinen, Topi Paananen, Paul Christian Bürkner, Aki Vehtari
2024
STATISTICS AND COMPUTING
Modeling public opinion over time and space : Trust in state institutions in Europe, 1989-2019
Marta Kołczyńska, Paul Christian Bürkner, Lauren Kennedy, Aki Vehtari
2024
Survey Research Methods
Advances in projection predictive inferenc
Yann McLatchie, Sölvi Rögnvaldsson, Frank Weber, Aki Vehtari
2024
Statistical Science
Efficient estimation and correction of selection-induced bias with order statistics
Yann McLatchie, Aki Vehtari
2024
STATISTICS AND COMPUTING
Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models
Ryoko Noda, Michael Francis Mechenich, Juha Saarinen, Aki Vehtari, Indrė Žliobaitė
2024
Ecological Informatics
Past, Present and Future of Software for Bayesian Inference
Erik Štrumbelj, Alexandre Bouchard-Côté, Jukka Corander, Andrew Gelman, Håvard Rue, Lawrence Murray, Henri Pesonen, Martyn Plummer, Aki Vehtari
2024
Statistical Science
Pareto Smoothed Importance Sampling
A Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry
2024
Journal of Machine Learning Research
Projection predictive variable selection for discrete response families with finite support
Frank Weber, Änne Glass, Aki Vehtari
2024
Computational Statistics