chagag

var data = {self: 1, group : [5,5,6,7,8,9]}

var model = function(){
  // uninformative prior over group beliefs
  var groupMean = uniform(0,10) //prior
  var groupSd = uniform(0,10)   //prior
  var belong = flip()           //prior


  // estimate groupmean and sd considering the group

chagag

# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

chagag

var data = {self: 1, group : [5,5,6,7,8,9]}


var model = function(){
  // uninformative prior over group beliefs
  var groupMean = uniform(0,10) //prior
  var groupSd = uniform(0, 10) //prior

  // observe
  // estimate groupmean and sd considering the group