Logarithm of bivariate normal density

Functions for Learning Bayesian Inference

94

0

Computes the log posterior density for the difference and sum of logits in a 2x2 contingency table for independent binomial samples and uniform prior placed on the logits

1 | ```
logctablepost(theta,data)
``` |

`theta` |
vector of parameter values "difference of logits" and "sum of logits") |

`data` |
vector containing number of successes and failures for first sample, and then second sample |

value of the log posterior

Jim Albert

1 2 3 4 | ```
s1=6; f1=2; s2=3; f2=10
data=c(s1,f1,s2,f2)
theta=c(2,4)
logctablepost(theta,data)
``` |

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