Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of
challenging problems. However, since deep learning methods operate as black boxes, the …

Well-designed experiments are likely to yield compelling evidence with efficient sample
sizes. Bayes Factor Design Analysis (BFDA) is a recently developed methodology that …

The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of
the topic and the multitude of ingredients involved therein, besides the complexity of turning …

Bayesian parameter estimation and Bayesian hypothesis testing present attractive
alternatives to classical inference using confidence intervals and p values. In part I of this …

Experiments in research on memory, language, and in other areas of cognitive science are
increasingly being analyzed using Bayesian methods. This has been facilitated by the …

Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide
one general way to compare different hypotheses by their compatibility with the observed …

The practical advantages of Bayesian inference are demonstrated here through two
concrete examples. In the first example, we wish to learn about a criminal's IQ: a problem of …

Self-regulated learning theory acknowledges the importance of an individual's
metacognitive monitoring and group-level regulation for learning achievement in …

The use of Bayes factors is becoming increasingly common in psychological sciences. Thus,
it is important that researchers understand the logic behind the Bayes factor in order to …

The open science movement is rapidly changing the scientific landscape. Because exact
definitions are often lacking and reforms are constantly evolving, accessible guides to open …