Bayes' Theorem.

Estimate marginal and conditional probabilities using Bayes theorem.

Usage

pA(pAgivenB, pB, pAgivenNotB)

pB(pBgivenA, pA, pBgivenNotA)

pAgivenB(pBgivenA, pA, pB = NULL, pBgivenNotA = NULL)

pBgivenA(pAgivenB, pB, pA = NULL, pAgivenNotB = NULL)

pAgivenNotB(pAgivenB, pA, pB)

pBgivenNotA(pBgivenA, pA, pB)

Arguments

pAgivenB

The conditional probability of A given B.

pB

The marginal probability of event B.

pAgivenNotB

The conditional probability of A given NOT B.

pBgivenA

The conditional probability of B given A.

pA

The marginal probability of event A.

pBgivenNotA

The conditional probability of B given NOT A.

Value

The requested marginal or conditional probability. One of:

• the marginal probability of A

• the marginal probability of B

• the conditional probability of A given B

• the conditional probability of B given A

• the conditional probability of A given NOT B

• the conditional probability of B given NOT A

Details

Estimates marginal or conditional probabilities using Bayes theorem.

See also

Other bayesian: deriv_d_negBinom()

Examples

pA(pAgivenB = .95, pB = .285, pAgivenNotB = .007171515)
#> [1] 0.2758776

pB(pBgivenA = .95, pA = .285, pBgivenNotA = .007171515)
#> [1] 0.2758776

pAgivenB(pBgivenA = .95, pA = .285, pB = .2758776)
#> [1] 0.9814135
pAgivenB(pBgivenA = .95, pA = .285, pBgivenNotA = .007171515)
#> [1] 0.9814134
pAgivenB(pBgivenA = .95, pA = .003, pBgivenNotA = .007171515)
#> [1] 0.285
pAgivenB(pBgivenA = 1/3, pA = 9/10, pBgivenNotA = 1)
#> [1] 0.75

pBgivenA(pAgivenB = .95, pB = .285, pA = .2758776)
#> [1] 0.9814135
pBgivenA(pAgivenB = .95, pB = .285, pAgivenNotB = .007171515)
#> [1] 0.9814134
pBgivenA(pAgivenB = .95, pB = .003, pAgivenNotB = .007171515)
#> [1] 0.285
pBgivenA(pAgivenB = 1/3, pB = 9/10, pAgivenNotB = 1)
#> [1] 0.75

pAgivenNotB(pAgivenB = .95, pB = .003, pA = .01)
#> [1] 0.007171515

pBgivenNotA(pBgivenA = .95, pA = .003, pB = .01)
#> [1] 0.007171515