Bayesian model with Bayesian Estimator


DESCRIPTION

Question 1: Create a Dataframe having 8 columns namely, A,B,C,D,E,F,G,H with the following properties: 1) Number of rows: 2500, Number of columns: 8 2) Create a Dataframe with random integers ranges from 0 to 3 3) Values of Column A = Column B + Column C 4) Values of Column H = Column G - Column A Question 2:  For the dataframe  created in Q1, create a new dataframe by choosing following columns [A, B, C, D, E, F], create a bayesian model with the following edges ('A', 'B'), ('C', 'B'), ('C', 'D'),('E', 'F'). Estimate the model parameters of node 'B' using Bayesian Estimator.  Carry out the following tasks: 1. Create a dataframe as instructed in question 1  2. Create a new dataframe by choosing the following columns [A, B, C, D, E, F] 3. Create a Bayesian model with the following edges ('A', 'B'), ('C', 'B'), ('C', 'D'),('E', 'F') 4. Estimate the model parameters of node 'B' using Bayesian Estimator 5. Write all the output in a .csv file

question1
#To be given to students
import pandas as pd
import numpy as np
import random
np.random.seed(2)
data = pd.DataFrame()
##Write your code here
question2
from pgmpy.models import BayesianModel
from pgmpy.estimators import BayesianEstimator
#write code here
print(node_B_cpds)
#write output to a file
np.savetxt("/code/output/output2.csv", node_B_cpds, delimiter=",")

Solution:

Here is data frame

OR

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