Bayesian Networks and Probabilistic Inference Previous Year Questions (PYQs) for GATE DA: 3+ Solved Questions with Step-by-Step Solutions

    Solve 3+ Bayesian Networks and Probabilistic Inference previous year questions for GATE DA with answers and detailed solutions. Free sample questions below.

    Chapter Roadmap: Bayesian Networks and Probabilistic Inference

    Chapter Roadmap

    1. Bayesian Network Inference Algorithms and CPT Computation Current Topic. High importance. Master reading Conditional Probability Tables, constructing the joint distribution, and performing inference by enumeration or variable elimination.
    2. Bayesian Network Factorization and Conditional Independence Next Topic. Moderate importance. Learn how the graph structure dictates independence via d-separation, reducing computational complexity.

    Topic Hero: Inference and CPT Computation

    Topic Hero: Inference and CPT Computation

    A Bayesian Network is a compact representation of a joint probability distribution. However, to answer real questions (like "What is the probability of a fire given that the alarm is ringing?"), we must perform inference.

    This topic bridges the gap between the static graph and dynamic computation. It focuses on two foundational skills:

    • CPT Computation: Understanding how to read, size, and populate Conditional Probability Tables.
    • Inference Algorithms: Systematically combining these local tables to compute marginal or conditional probabilities of query variables.

    Bayesian Networks and Probabilistic Inference: Solved Questions with Step-by-Step Explanations (3 Problems)

    Question 1 · Artificial Intelligence MSQ

    Which of the following statements is/are correct in a Bayesian network?

    1. A.

      Variable elimination is an approximate inference algorithm

    2. B.

      Gibbs sampling is an exact inference algorithm

    3. C.

      Variable elimination is used to determine conditional probabilities

    4. D.

      Rejection sampling is an approximate inference algorithm

    Question 2 · Artificial Intelligence MCQ
    Consider five random variables and whose joint distribution
    satisfies:

    Which ONE of the following statements is FALSE?
    1. A.

      is conditionally independent of given

    2. B.

      is conditionally independent of given

    3. C.

      and are conditionally independent given

    4. D.

      and are conditionally independent given

    Question 3 · Artificial Intelligence NAT
    Given the following Bayesian Network consisting of four Bernoulli random
    variables and the associated conditional probability tables:
    UVWZP(·)U = 00.5U = 10.5P(V = 0| ·)P(V = 1| ·)U = 00.50.5U = 10.50.5P(W = 0| ·)P(W = 1| ·)U = 010U = 101P(Z = 0| ·)P(Z = 1| ·)V = 0W = 00.50.5V = 0W = 110V = 1W = 010V = 1W = 10.50.5
    The value of = ______ (rounded off to three
    decimal places).

    More previous year questions (pyqs) in this unit

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    Bayesian Networks and Probabilistic Inference Previous Year Questions (PYQs) for GATE DA: 3+ Solved Questions with Step-by-Step Solutions

    Solve 3+ Bayesian Networks and Probabilistic Inference previous year questions for GATE DA with answers and detailed solutions. Free sample questions below.

    A question from this chapter

    Question 1

    Which of the following statements is/are correct in a Bayesian network?

    Question 2
    Consider five random variables and whose joint distribution
    satisfies:

    Which ONE of the following statements is FALSE?
    Question 3
    Given the following Bayesian Network consisting of four Bernoulli random
    variables and the associated conditional probability tables:
    UVWZP(·)U = 00.5U = 10.5P(V = 0| ·)P(V = 1| ·)U = 00.50.5U = 10.50.5P(W = 0| ·)P(W = 1| ·)U = 010U = 101P(Z = 0| ·)P(Z = 1| ·)V = 0W = 00.50.5V = 0W = 110V = 1W = 010V = 1W = 10.50.5
    The value of = ______ (rounded off to three
    decimal places).
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