dirichlet-concat-divergence.py

dirichlet-concat-divergence

## Combined model
c_comb = np.asarray([[16,29,4],
                [16,29,6],
                [14,30,4],
                [16,29,3],
                [16,31,5],
                [13,29,5],
                [15,32,5],
                [15,29,6],
                [17,31,6],
                [13,29,4],
                [15,4,31],
                [15,3,30],
                [15,5,31],
                [16,6,31],
                [14,5,29],
                [16,5,30],
                [15,6,30],
                [13,6,30],
                [14,6,31],
                [16,6,29],
                [15,7,29]]);

counts_comb = np.asarray([49,51,48,48,52,47,52,50,54,46,50,48,51,53,48,51,51,49,51,51,51])[:, None];
idx = np.asarray([0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1]).astype(int);

with pm.Model() as model_comb:
    
    hyper_param_comb1 = pm.HalfNormal('hyper_param_comb1',10,shape=3);
    hyper_param_comb2 = pm.HalfNormal('hyper_param_comb2',10,shape=3);

    param_comb1 = pm.Dirichlet('param_comb1',a=hyper_param_comb1,shape=(N1,3));
    param_comb2 = pm.Dirichlet('param_comb2',a=hyper_param_comb2,shape=(N2,3));
    
    param_comb = tt.concatenate((param_comb1,param_comb2),axis=0);
    print(param_comb.tag.test_value.shape)
    
    y_comb = pm.Multinomial('y_comb', n=counts_comb, p=param_comb[idx], observed=c_comb);
    trace_comb = pm.sample(target_accept=0.90);