Bayesian models and conditioning

Fundamentals of Bayesian Data Analysis in R

Rasmus Bååth

Data Scientist

n_samples <- 100000
n_ads_shown <- 100
proportion_clicks <- runif(n_samples, min = 0.0, max = 0.2)
n_visitors <- rbinom(n = n_samples, size = n_ads_shown, 
                     prob = proportion_clicks)

n_samples <- 100000
n_ads_shown <- 100
proportion_clicks <- runif(n_samples, min = 0.0, max = 0.2)
n_visitors <- rbinom(n = n_samples, size = n_ads_shown, 
                     prob = proportion_clicks)
prior <- data.frame(proportion_clicks, n_visitors)

prior

   proportion_clicks n_visitors
1             0.12         10
2             0.04          3
3             0.11         14
4             0.15         14
5             0.15         12
6             0.16         13
7             0.04          6
8             0.04          3
9             0.09         10
10            0.04          3
11            0.08          8
12            0.13         12
13            0.02          3
14            0.18         19
15            0.04          5
16            0.10         10
...           ...          ...

The essence of Bayesian inference

Bayesian inference is conditioning on data, in order to learn about parameter values.

Try some Bayesian inference yourself!

Fundamentals of Bayesian Data Analysis in R