Uncertainity - Inference

 Introduction: 

Inference means something that is inferred. The act of passing from one proposition, statement, or judgment considered as true to another whose truth is believed to follow from that of the former

We want to compute the probability distribution of the Appointment variable is given the evidence that there are light rain and no track maintenance. That is, we know that there is light rain and no track maintenance, and we want to figure out what are the probabilities that we attend the appointment and that we miss the appointment, P(Appointment | light, no). from the joint probability section, we know that we can express the possible values of the Appointment random variable as a proportion, rewriting P(Appointment | light, no) as αP(Appointment, light, no). How can we calculate the probability distribution of Appointment if its parent is only the Train variable, and not Rain or Maintenance? Here, we will use marginalization. The value of P(Appointment, light, no) is equal to α[P(Appointment, light, no, delayed) + P(Appointment, light, no, on time)].

This is an Artificial Intelligence course conducted in City Universit by Nuruzzaman Faruqui. This is the best course in Bangladesh. Because Sir explained all the necessary things to us well. The problem we are dealing with in the lab. First, sir gives us a very good idea of the problem by using many examples. After that sir explains about the solutions like how we can solve the problem, how they work. For that, we understand everything very easily.

The python code to implement Game of Clue 

First, we have to import the model file. Where we create a Bayesian Network  

 Pseudocode :

from pomegranate import *

# Rain node has no parent
rain = Node(DiscreteDistribution({
    "none": 0.7,
    "light": 0.2,
    "heavy": 0.1
}), name="rain")

# Maintenance node is conditional on rain for that we use
  Conditional ProbabilityTable
maintenance = Node(ConditionalProbabilityTable([
    ["none", "yes", 0.4],
    ["none", "no", 0.6],
    ["light", "yes", 0.2],
    ["light", "no", 0.8],
    ["heavy", "yes", 0.1],
    ["heavy", "no", 0.9]
], [rain.distribution]), name="maintenance")

# Train node is conditional on rain and maintenance
train = Node(ConditionalProbabilityTable([
    ["none", "yes", "on time", 0.8],
    ["none", "yes", "delayed", 0.2],
    ["none", "no", "on time", 0.9],
    ["none", "no", "delayed", 0.1],
    ["light", "yes", "on time", 0.6],
    ["light", "yes", "delayed", 0.4],
    ["light", "no", "on time", 0.7],
    ["light", "no", "delayed", 0.3],
    ["heavy", "yes", "on time", 0.4],
    ["heavy", "yes", "delayed", 0.6],
    ["heavy", "no", "on time", 0.5],
    ["heavy", "no", "delayed", 0.5],
], [rain.distribution, maintenance.distribution]), name="train")

# Appointment node is conditional on train
appointment = Node(ConditionalProbabilityTable([
    ["on time", "attend", 0.9],
    ["on time", "miss", 0.1],
    ["delayed", "attend", 0.6],
    ["delayed", "miss", 0.4]
], [train.distribution]), name="appointment")

# Now create a Bayesian Network and add states

model = BayesianNetwork()
model.add_states(rain, maintenance, train, appointment)

# Add edges connecting nodes
model.add_edge(rain, maintenance)
model.add_edge(rain, train)
model.add_edge(maintenance, train)
model.add_edge(train, appointment)

# Finalize model
model.bake()

Now, need to write the inference source code. First, need to import model from the model.

from model import model

# Calculate predictions
predictions = model.predict_proba({
    "train": "delayed"
})

# Print predictions for each node
for node, prediction in zip(model.states, predictions):
    if isinstance(prediction, str):
        print(f"{node.name}: {prediction}")
    else:
        print(f"{node.name}")
        for value, probability in prediction.parameters[0].items():
            print(f"       {value} : {probability: 0.4f}")

Result : 

If everything fine then we will get this type of result. 

Conclusion : 

First, we introduced the problem. After that, we explain the problem and how we can solve the problem. then we explain the algorithm after that we write the code and give comments that's why you can easily understand the code. I hope anyone can easily understand the code. we discuss the result. If anyone follows this article then he/she can easily understand everything, They can also do this very easily. 

we can say this is the only reflection of the way that Sir has taught us. So this is the best AI course in Bangladesh. 

 

You are free to copy the code from here or some other concluding remarks