farshadl123

with pm.Model() as model:
    
    ## Independent priors
    alpha = pm.Normal('Person', mu = 0, sigma = 3, shape = (1, len(data)))
    gamma = pm.Normal('Question', mu = 0, sigma = 3, shape = (data.shape[1], 1))
    
    ## Log-Likelihood
    def logp(d):
        v1 = tt.transpose(d) * tt.log(tt.nnet.sigmoid(alpha - (gamma - gamma.mean(0))))
        v2 = tt.transpose((1-d)) * tt.log(1 - tt.nnet.sigmoid(alpha - (gamma - gamma.mean(0))))

farshadl123

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",