#### Learning and Cognitive Systems

# CHURCH-Code - Mixture Model of Two Binomial Models - k1=5; k2=0,...,10; n1=n2=10

We have two alternative hypotheses concerning the causal process of generating two sets of successes out of n1 and n trials:

**Hypothesis 1: **There a *two*** **processes with rates *theta1* and *theta2* generating k1 and k2 successes out of n1 and n2 trials. The hypothesis is represented by the **'full' model 1**.

**Hypothesis 2: **There is *one* process with common rate *theta* generating k1 and k2 successes out of n1 and n2 trials. The hypothesis is represented by the **'restricted' model 2**.

We combine *both* models to a **mixture model**. The prior probability for each of the two submodels is P(Model) = 0.5.

A pure and simple Bayesian analysis demonstrates the plausibility of each model by computing the **posterior probability P(Model | k1, k2, n1, n2)**. By a numerical example with k1=5; k2=0, ... ,10; n1=n2=10 we can show that the posterior probability in favor of each model switches first between k2=2 and k2=3 and second between k2=7 and k2=8. For the range k2=3-7 the *restricted* model has a higher posterior probability and for k2=0-2, 8-10 the *full *model is more plausible.

The CHURCH-Code can be found here.

# CHURCH-Code and -output for Mixture Model of Two Binomial Models with k1=0; k2=0,...,10; n1=n2=10

We have two alternative hypotheses concerning the causal process of generating two sets of successes out of n1 and n trials:

**Hypothesis 1: **There a *two*** **processes with rates *theta1* and *theta2* generating k1 and k2 successes out of n1 and n2 trials. The hypothesis is represented by the **'full' model 1**.

**Hypothesis 2: **There is *one* process with common rate *theta* generating k1 and k2 successes out of n1 and n2 trials. The hypothesis is represented by the **'restricted' model 2**.

We combine *both* models to a **mixture model**. The prior probability for each of the two submodels is P(Model) = 0.5.

A pure and simple Bayesian analysis demonstrates the plausibility of each model by computing the **posterior probability P(Model | k1, k2, n1, n2)**. By using a numerical example with **k1=0**; k2=0, ... ,10; n1=n2=10 we can show that the posterior probability in favor of each model switches first between k2=2 and k2=3 and second between k2=7 and k2=8. For the range k2=3-7 the *restricted* model has a higher posterior probability and for k2=0-2, 8-10 the *full *model is more plausible.