Fundamentals of Bayesian Data Analysis in R
Rasmus Bååth
Data Scientist
rbinom
and rpois
n_visitors
= 13 | prob_success
= 10%)n_visitors <- rbinom(n = 100000, size = 100, prob = 0.1)
sum(n_visitors == 13) / length(n_visitors)
0.074
dbinom
and dpois
n_visitors
= 13 | prob_success
= 10%)dbinom(13, size = 100, prob = 0.1)
0.074
n_visitors
= 13 or n_visitors
= 14 | prob_success
= 10%)dbinom(13, size = 100, prob = 0.1) + dbinom(14, size = 100, prob = 0.1)
0.126
n_visitors
| prop_success
= 10%)n_visitors = seq(0, 100, by = 1)
probability <- dbinom(n_visitors, size = 100, prob = 0.1)
n_visitors
0 1 2 3 4 5 6 7 ...
probability
0.000 0.000 0.002 0.006 0.016 0.034 0.060 0.089 ...
plot(n_visitors, probability, type = "h")
x <- runif(n = 100000, min = 0.0, max = 0.2)
hist(x)
runif
is dunif
:dunif(x = 0.12, min = 0.0, max = 0.2)
5
x = seq(0, 0.2, by=0.01)
dunif(x, min = 0.0, max = 0.2)
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
Fundamentals of Bayesian Data Analysis in R