Bayesian Networks MCQs With Answers

Computer MCQs 360May 20, 2024

0 5 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

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

Markov chain Monte Carlo

Kernel density estimation

MAP inference

2 / 40

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

Model overfitting

Data sparsity

Computational complexity

3 / 40

________ is used to integrate domain knowledge into Bayesian Networks.

Data preprocessing

Pattern recognition

Expert elicitation

4 / 40

What is a Bayesian Network (BN)?

Neural network

Probabilistic graphical model

Decision tree

5 / 40

What does d-separation determine in Bayesian Networks?

Correlation

Causality

Independence

6 / 40

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

Gradient boosting

Clustering algorithm

Imputation

7 / 40

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

Probabilistic reasoning

Deterministic logic

Heuristic search

8 / 40

________ is a common application of Bayesian Networks in healthcare.

Image classification

Medical diagnosis

Natural language processing

9 / 40

What is the role of conditional probabilities in Bayesian Networks?

Quantify relationships

Optimize functions

Classify data points

10 / 40

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

Marginalization property

Local Markov property

Global Markov property

11 / 40

What is the challenge of learning Bayesian Networks from data?

Local minima

Curse of dimensionality

Overfitting

12 / 40

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

Synchronous

Recursive

Acyclic

13 / 40

What is the purpose of conditional independence in Bayesian Networks?

Simplify computation

Increase model complexity

Normalize data

14 / 40

In a Bayesian Network, nodes represent _________.

Cluster centers

Random variables

Data points

15 / 40

________ is used to propagate evidence in Bayesian Networks efficiently.

Belief propagation

Ensemble learning

Dimensionality reduction

16 / 40

________ involves adjusting Bayesian Network parameters based on observed data.

Parameter learning

Hyperparameter tuning

Model validation

17 / 40

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

Optimize computation

Shield node from others

Filter noisy data

18 / 40

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

Update beliefs

Perform clustering

Train neural networks

19 / 40

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

Cross-validation

Validation

Regularization

20 / 40

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

Chain rule

Gradient descent

Clustering algorithm

21 / 40

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

Probability distribution

Optimization function

Regression model

22 / 40

What is the role of prior probabilities in Bayesian Networks?

Initial beliefs

Final predictions

Error correction

23 / 40

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

Regularization

Imputation

Data augmentation

24 / 40

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

Parameter learning

Feature selection

Model validation

25 / 40

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

Frequentist approach

Probabilistic reasoning

Deterministic logic

26 / 40

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

Structure learning

Feature extraction

Random sampling

27 / 40

________ are directed edges connecting nodes in a Bayesian Network.

Dashes

Lines

Arrows

28 / 40

What does a higher-order Bayesian Network model represent?

Complex decision making

Hierarchical structure

Dependencies among clusters

29 / 40

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

Approximate inference

Ensemble learning

Cross-validation

30 / 40

What does the structure of a Bayesian Network represent?

Activation functions

Data distributions

Conditional dependencies

31 / 40

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

Data augmentation

Regularization

Gradient descent

32 / 40

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

Convergence criteria

Regularization

Cross-validation

33 / 40

________ are variables with no parents in a Bayesian Network.

Hidden nodes

Root nodes

Leaf nodes

34 / 40

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

Bayesian inference

Reinforcement learning

Principal component analysis

35 / 40

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

Independence

Graph structure

Conditional dependencies

36 / 40

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

Probabilistic reasoning

Deterministic logic

Fuzzy logic

37 / 40

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

Bayes' theorem

Thomas Bayes

Bayes' law

38 / 40

________ is used to handle continuous variables in Bayesian Networks.

Support vector machine

Decision tree

Gaussian distribution

39 / 40

________ in a Bayesian Network indicates direct influence between nodes.

Correlation

Conditional dependency

Causation

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 5 1 minute read