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Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice

Scheibehenne, Benjamin ; Pachur, Thorsten

In: Psychonomic Bulletin & Review, 2015, vol. 22, no. 2, p. 391-407

Hierarchical prediction errors in midbrain and septum during social learning

Diaconescu, Andreea O. ; Mathys, Christoph ; Weber, Lilian A. E. ; Kasper, Lars ; Mauer, Jan ; Stephan, Klaas E.

In: Social Cognitive and Affective Neuroscience, 2017, vol. 12, no. 4, p. 618-634

Pathological Gambling Induced by Dopamine Antagonists: A Case Report

Grötsch, Philipp ; Lange, Claudia ; Wiesbeck, Gerhard ; Lang, Undine

In: Journal of Gambling Studies, 2015, vol. 31, no. 1, p. 295-297

Bayesian inference on stochastic gene transcription from flow cytometry data

Tiberi, Simone ; Walsh, Mark ; Cavallaro, Massimo ; Hebenstreit, Daniel ; Finkenstädt, Bärbel

In: Bioinformatics, 2018, vol. 34, no. 17, p. i647-i655

A Bayesian approach for comparing cross-validated algorithms on multiple data sets

Corani, Giorgio ; Benavoli, Alessio

In: Machine Learning, 2015, vol. 100, no. 2-3, p. 285-304

A Descriptive Study of Gambling Among Emerging Adult Males in French-Speaking Switzerland

Tomei, Alexander ; Tichelli, Emmanuel ; Ewering, Neil ; Nunweiler-Hardegger, Sophie ; Simon, Olivier

In: Journal of Gambling Studies, 2015, vol. 31, no. 3, p. 607-620

Bridging Inter- and Intraspecific Trait Evolution with a Hierarchical Bayesian Approach

Kostikova, Anna ; Silvestro, Daniele ; Pearman, Peter B. ; Salamin, Nicolas

In: Systematic Biology, 2016, vol. 65, no. 3, p. 417-431

Accelerating Bayesian inference for evolutionary biology models

Meyer, Xavier ; Chopard, Bastien ; Salamin, Nicolas ; Kelso, Janet ; Kelso, Janet

In: Bioinformatics, 2017, vol. 33, no. 5, p. 669-676

Analyzing system performance with probabilistic performance annotations

Rogora, Daniele ; Carzaniga, Antonio (Dir.) ; Soulé, Robert (Codir.)

Thèse de doctorat : Università della Svizzera italiana, 2020 ; 2020INFO024.

Understanding the performance of software is complicated. For several performance metrics, in addition to the algorithmic complexity, one must also consider the dynamics of running a program within different combinations of hardware and software environments. Such dynamical aspects are not visible from the code alone, and any kind of static analysis falls short. For example, in reality, the...

Bayesian dependence tests for continuous, binary and mixed continuous-binary variables

Benavoli, Alessio ; De Campos, Cassio P.

In: Entropy, 2016, vol. 18, no. 9, p. 326

Tests for dependence of continuous, discrete and mixed continuous-discrete variables are ubiquitous in science. The goal of this paper is to derive Bayesian alternatives to frequentist null hypothesis significance tests for dependence. In particular, we will present three Bayesian tests for dependence of binary, continuous and mixed variables. These tests are nonparametric and based on the...