Application of Bayesian Updating to sequential learning tasks
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#Bayesian Updating as Escalation This project provides the basis to simulate a series of wins and losses.

Data can be simulated and then Bayesian Updating can be applied to lagged learning sequences (i.e., Two-, three-, and four-lagged projections). Simulated data take the form of sequential learning tasks. In randomized sequences of events, a distinct pattern of Wins or Losses is observed and then modeled using Bayesian Updating to determine warranted beliefs.

Warranted beliefs, in Bayesian thinking, refer to the likelihood that an action is likely to succeed taking into account earlier, similar circumstances. As such, warranted beliefs are differentially affected by the amount of exposures as well as the outcomes from each exposure.

This repository serves as sample usage of Bayesian Updating to model escalation/persistence in no-win situations.

##Works utilized elsewhere ISAAC, PHP-port by Illmari Karonen original work by Bob Jenkins - unmodified; shared on Stack Overflow

##License This project is licensed under the GPL-V2+ license.

Developed by Shawn Patrick Gilroy, PhD NCSP BCBA-D Published in Journal of Behavioural Processes (Shawn Gilroy, Donald Hantula)
Temple University, 2016