You've fitted a Bayesian Normal model!

Fundamentals of Bayesian Data Analysis in R

Rasmus Bååth

Data Scientist

BEST

A Bayesian model developed by John Kruschke.
Assumes the data comes from a t-distribution.

BEST

A Bayesian model developed by John Kruschke.
Assumes the data comes from a t-distribution.
Estimates the mean, standard deviation and degrees-of-freedom parameter.
library(BEST)
Uses Markov chain Monte Carlo (MCMC).

Let's use BEST!

library(BEST)
iq <- c(55, 44, 34, 18, 51, 40, 40, 49, 48, 46)

Let's use BEST!

library(BEST)
iq <- c(55, 44, 34, 18, 51, 40, 40, 49, 48, 46)
fit <- BESTmcmc(iq)

Let's use BEST!

library(BEST)
iq <- c(55, 44, 34, 18, 51, 40, 40, 49, 48, 46)
fit <- BESTmcmc(iq)
fit

MCMC fit results for BEST analysis:

       mean     sd median  HDIlo HDIup  
mu    43.15  3.810  43.28 35.367 50.49 
nu    27.42 26.647  18.91  1.001 81.59 
sigma 11.00  3.754  10.44  4.857 18.38

Let's use BEST!

library(BEST)
iq <- c(55, 44, 34, 18, 51, 40, 40, 49, 48, 46)
fit <- BESTmcmc(iq)
plot(fit)

Try out BEST yourself!

Fundamentals of Bayesian Data Analysis in R