Posterior updating of two proportions

Computes posterior distribution of two proportions with a discrete prior

two_p_update(prior, s1f1, s2f2)

Arguments

prior	prior probability matrix where the rows and columns are labeled with the values of the proportions
s1f1	number of successes and number of failures from first sample
s2f2	number of successes and number of failures from second sample

Value

posterior probability matrix

Examples

  prior <- testing_prior()
  first_sample <- c(3, 10)
  second_sample <- c(8, 20)
  two_p_update(prior, first_sample, second_sample)
#>              0.1          0.2          0.3          0.4          0.5
#> 0.1 4.208081e-03 1.276979e-02 2.265034e-02 1.036690e-02 1.611774e-03
#> 0.2 1.295862e-03 2.516740e-01 5.580069e-02 2.553957e-02 3.970716e-03
#> 0.3 1.150570e-03 2.793205e-02 3.963546e-01 2.267608e-02 3.525520e-03
#> 0.4 5.837966e-04 1.417265e-02 2.513866e-02 9.204629e-02 1.788840e-03
#> 0.5 1.841531e-04 4.470629e-03 7.929755e-03 3.629392e-03 4.514182e-03
#> 0.6 3.416825e-05 8.294921e-04 1.471307e-03 6.734068e-04 1.046966e-04
#> 0.7 3.055454e-06 7.417631e-05 1.315698e-04 6.021857e-05 9.362367e-06
#> 0.8 7.909317e-08 1.920121e-06 3.405804e-06 1.558812e-06 2.423533e-07
#> 0.9 1.099757e-10 2.669845e-09 4.735624e-09 2.167461e-09 3.369819e-10
#>              0.6          0.7          0.8          0.9
#> 0.1 7.990129e-05 8.696678e-07 7.611386e-10 1.862447e-15
#> 0.2 1.968424e-04 2.142487e-06 1.875118e-09 4.588268e-15
#> 0.3 1.747724e-04 1.902271e-06 1.664880e-09 4.073832e-15
#> 0.4 8.867908e-05 9.652077e-07 8.447557e-10 2.067052e-15
#> 0.5 2.797298e-05 3.044657e-07 2.664703e-10 6.520321e-16
#> 0.6 4.152143e-05 5.649135e-08 4.944158e-11 1.209797e-16
#> 0.7 4.641254e-07 4.041336e-08 4.421253e-12 1.081846e-17
#> 0.8 1.201430e-08 1.307670e-10 9.155848e-13 2.800457e-19
#> 0.9 1.670537e-11 1.818259e-13 1.591352e-16 3.115132e-21