Bayesian Networks MCQs With Answers

Computer MCQs 360May 20, 2024

0 7 1 minute read

Welcome to the Bayesian Networks MCQs with Answers. In this post, we have shared Bayesian Networks Online Test for different competitive exams. Find practice Bayesian Networks Practice Questions with answers in Computer Tests exams here. Each question offers a chance to enhance your knowledge regarding Bayesian Networks.

Bayesian Networks Online Quiz

By presenting 3 options to choose from, Bayesian Networks Quiz which cover a wide range of topics and levels of difficulty, making them adaptable to various learning objectives and preferences. You will have to read all the given answers of Bayesian Networks Questions and Answers and click over the correct answer.

Test Name: Bayesian Networks MCQ Quiz Practice
Type: Quiz Test
Total Questions: 40
Total Marks: 40
Time: 40 minutes

Note: Answer of the questions will change randomly each time you start the test. Practice each quiz test at least 3 times if you want to secure High Marks. Once you are finished, click the View Results button. If any answer looks wrong to you in Quizzes. simply click on question and comment below that question. so that we can update the answer in the quiz section.

Bayesian Networks

Please fill out the form before starting Quiz.

1 / 40

What is the limitation of Bayesian Networks in handling large datasets?

Data sparsity

Computational complexity

Model overfitting

2 / 40

________ involves determining the most likely explanation given evidence in Bayesian Networks.

Kernel density estimation

Markov chain Monte Carlo

MAP inference

3 / 40

What is the role of conditional probabilities in Bayesian Networks?

Classify data points

Quantify relationships

Optimize functions

4 / 40

What is a Bayesian Network (BN)?

Neural network

Probabilistic graphical model

Decision tree

5 / 40

________ is used to represent uncertainty about the value of a variable in Bayesian Networks.

Regression model

Optimization function

Probability distribution

6 / 40

________ is a technique to infer missing values in Bayesian Networks.

Gradient boosting

Imputation

Clustering algorithm

7 / 40

________ in a Bayesian Network indicates direct influence between nodes.

Conditional dependency

Causation

Correlation

8 / 40

In a Bayesian Network, nodes represent _________.

Cluster centers

Random variables

Data points

9 / 40

________ is a property of Bayesian Networks where nodes are conditionally independent given their parents.

Global Markov property

Local Markov property

Marginalization property

10 / 40

What does the term "Bayesian" in Bayesian Networks refer to?

Bayes' law

Thomas Bayes

Bayes' theorem

11 / 40

________ is a property of Bayesian Networks where no node has a direct or indirect connection back to itself.

Recursive

Acyclic

Synchronous

12 / 40

________ is a technique to handle missing data in Bayesian Networks.

Imputation

Data augmentation

Regularization

13 / 40

What is the advantage of using Bayesian Networks for reasoning under uncertainty?

Heuristic search

Deterministic logic

Probabilistic reasoning

14 / 40

What is the role of prior probabilities in Bayesian Networks?

Final predictions

Error correction

Initial beliefs

15 / 40

What does d-separation determine in Bayesian Networks?

Correlation

Independence

Causality

16 / 40

________ involves adjusting Bayesian Network parameters based on observed data.

Hyperparameter tuning

Parameter learning

Model validation

17 / 40

________ is a method to validate Bayesian Network models with real-world data.

Regularization

Cross-validation

Validation

18 / 40

What does the structure of a Bayesian Network represent?

Data distributions

Activation functions

Conditional dependencies

19 / 40

What is the main difference between Bayesian Networks and Markov Networks?

Graph structure

Conditional dependencies

Independence

20 / 40

What does a higher-order Bayesian Network model represent?

Complex decision making

Dependencies among clusters

Hierarchical structure

21 / 40

________ is used to propagate evidence in Bayesian Networks efficiently.

Belief propagation

Dimensionality reduction

Ensemble learning

22 / 40

________ are variables with no parents in a Bayesian Network.

Root nodes

Leaf nodes

Hidden nodes

23 / 40

________ is used to integrate domain knowledge into Bayesian Networks.

Data preprocessing

Expert elicitation

Pattern recognition

24 / 40

________ is a type of reasoning that deals with uncertain information.

Fuzzy logic

Probabilistic reasoning

Deterministic logic

25 / 40

________ is used to handle continuous variables in Bayesian Networks.

Support vector machine

Decision tree

Gaussian distribution

26 / 40

What is the challenge of learning Bayesian Networks from data?

Overfitting

Curse of dimensionality

Local minima

27 / 40

________ is used to update beliefs in Bayesian Networks with new evidence.

Principal component analysis

Bayesian inference

Reinforcement learning

28 / 40

________ is a method to learn conditional probability tables in Bayesian Networks from data.

Model validation

Parameter learning

Feature selection

29 / 40

________ are directed edges connecting nodes in a Bayesian Network.

Dashes

Arrows

Lines

30 / 40

What is the purpose of the Markov Blanket in Bayesian Networks?

Optimize computation

Filter noisy data

Shield node from others

31 / 40

________ is a method to learn the structure of a Bayesian Network from data.

Structure learning

Feature extraction

Random sampling

32 / 40

________ is a technique to improve the scalability of Bayesian Network inference.

Ensemble learning

Cross-validation

Approximate inference

33 / 40

________ is a technique to prevent overfitting in Bayesian Network models.

Regularization

Gradient descent

Data augmentation

34 / 40

What does the term "Bayesian" in Bayesian Networks emphasize?

Deterministic logic

Probabilistic reasoning

Frequentist approach

35 / 40

What is the purpose of conditional independence in Bayesian Networks?

Normalize data

Simplify computation

Increase model complexity

36 / 40

________ is used to calculate the joint probability distribution in a Bayesian Network.

Gradient descent

Chain rule

Clustering algorithm

37 / 40

________ is a common application of Bayesian Networks in healthcare.

Natural language processing

Medical diagnosis

Image classification

38 / 40

________ is a method to ensure convergence in Bayesian Network learning algorithms.

Regularization

Cross-validation

Convergence criteria

39 / 40

What is the purpose of the evidence propagation algorithm in Bayesian Networks?

Train neural networks

Update beliefs

Perform clustering

40 / 40

________ is a property of Bayesian Networks where the absence of an edge indicates conditional independence.

Markov condition

Chain rule

Central limit theorem

Download Certificate of Quiz Bayesian Networks

On the end of Quiz, you can download the certificate of the quiz if you got more than 70% marks. Add a certificate to your job application or social profile (like LinkedIn) and get more job offers.

Download Bayesian Networks MCQs with Answers Free PDF

You can also download 100 Bayesian Networks Questions with Answers free PDF from the link provided below. To Download file in PDF click on the arrow sign at the top right corner.

If you are interested to enhance your knowledge regarding English, Physics, Chemistry, and Biology please click on the link of each category, you will be redirected to dedicated website for each category.

Computer MCQs 360May 20, 2024

0 7 1 minute read