Link: https://bayesball.github.io/nsf_web/jscript/p_discrete/prior2a.htm
Description: This applet assumes that the proportion takes one of the 11 values 0, 0.1, 0.2, …, 1. One can input the prior and update the probabilities given the number of successes and failures in the data.
Link: https://bayesball.shinyapps.io/ChooseBetaPrior_3/
Description: The user chooses the median and 90th percentile of the prior for a proportion and the app displays the beta curve that matches this information. In addition, the app displays “middle” probability intervals and displays the prior predictive distribution.
Link: https://bayesball.github.io//nsf_web/jscript/m_discrete/mdisc.htm
Description: This applet assumes that the mean takes one of a discrete set of values. One can input the prior and update the probabilities given the sample mean, sample size, and known standard deviation.
Link: https://bayesball.shinyapps.io/Metropolis/
Description: The user chooses an arbitrary discrete probability distribution on the values 1, 2, 3, 4, and 5. Also the user chooses the number of iterations. The app displays a trace plot of iterations of a Metropolis random walk algorithm and displays a histogram of the simulated draws.