The authors used a Bayesian modeling framework to fit behavior and serotonin neuron activity to reward history across multiple timescales. A key goal was to distinguish value coding from other ...

SERC copy Purchased with Adopt-a-Book funds. "Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the ...

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

These questions come from my Udemy training and the certificationexams.pro website, resources that have helped many students pass the DP-100 certification. These are not DP-100 exam dumps or ...

Optimal cue integration, Bayesian Causal Inference, spatial orienting, speech illusions and other key phenomena in audiovisual perception naturally emerge from the collective behavior of a population ...

Introduction Adolescence is a key period of development for mental health; however, little is known about how (cumulative) daily life experiences impact long-term mental health development in this ...

Bayesian inference is a statistical method of inductive reasoning based on the reassessment of competing hypotheses in the presence of new evidence. Conceptually similar to the scientific method ...