Bayesian Learning QUIZ (MCQ QUESTIONS AND ANSWERS)

Better handling of missing data

Faster convergence and lower computational complexity

Better exploration of the posterior distribution

More accurate posterior estimates

Uncertainty due to the inherent randomness in the data

Uncertainty due to the choice of model parameters

Uncertainty due to the limited amount of data available

Uncertainty due to the choice of the prior distribution

They cannot model non-linear relationships

They have high computational complexity

They require large amounts of training data

They cannot handle missing data

Gaussian Process Regression

Metropolis-Hastings

Gibbs sampling

Expected Improvement

A learning strategy that selects the most informative examples for labeling to improve the model

A learning strategy that continuously updates the model as new data becomes available

A learning strategy that actively explores the hypothesis space

A learning strategy that incorporates feedback from the user to improve the model

It is more computationally efficient than grid search or random search

It can incorporate prior knowledge about the optimization problem

It can handle noisy evaluations of the objective function

Both A and B

To estimate the prior distribution

To estimate the posterior distribution across models with different dimensions

To estimate the likelihood of the data given a hypothesis

To optimize model parameters

They provide uncertainty estimates for predictions

They are computationally inexpensive

They can be used for both supervised and unsupervised learning

They do not require prior knowledge of the data distribution

A non-parametric Bayesian method for regression and classification

A conjugate prior for continuous distributions

A prior distribution over functions

Both A and C

To estimate the prior distribution

To simplify the computation of posterior probabilities

To estimate the posterior distribution when exact methods are intractable

To compute the likelihood of the data given a hypothesis

The model parameters that maximize the prior probability

The model parameters that maximize the likelihood of the data

The model parameters that maximize the posterior probability

The model parameters that maximize the marginal likelihood

Difficulty in updating the posterior distribution

Inability to handle large datasets

Inability to incorporate prior knowledge

High computational complexity

To model uncertainty in the choice of model parameters

To serve as a conjugate prior for categorical distributions

To model uncertainty in the choice of the number of clusters

To serve as a conjugate prior for continuous distributions

The set of variables that directly influence the variable

The set of variables that the variable directly influences

The minimal set of variables that, when conditioned on, renders the variable independent of all other variables in the network

The set of variables that are conditionally independent of the variable

Expectation-Maximization

Metropolis-Hastings

Gibbs sampling

Hamiltonian Monte Carlo

The likelihood of a single data point given the model parameters

The likelihood of the data averaged over all possible model parameters

The likelihood of the data given the prior distribution

The likelihood of the data given the posterior distribution

Bayesian estimation incorporates prior information, while MLE does not

MLE is computationally more expensive than Bayesian estimation

Bayesian estimation is more interpretable than MLE

MLE can handle large datasets, while Bayesian estimation cannot

Combining multiple models based on their posterior probabilities to make predictions

Averaging the prior probabilities of multiple models

Averaging the likelihood functions of multiple models

Combining multiple models based on their likelihood functions to make predictions

Gradient descent

Expectation-Maximization

Metropolis-Hastings

Principal Component Analysis

To estimate the prior distribution

To optimize model parameters

To estimate the posterior distribution

To evaluate model fit

A graphical model representing the probabilistic relationships among a set of variables

A network of conjugate priors for Bayesian learning

A neural network with Bayesian learning algorithms

A network of hypotheses and their associated prior probabilities

The probability of the data given a hypothesis

The probability of a hypothesis before observing the data

The probability of a hypothesis given the data

The probability of the data independent of any hypothesis

To evaluate the fit of a model while penalizing model complexity

To compute the prior probability of a hypothesis

To compute the posterior probability of a hypothesis

To compute the likelihood of the data given a hypothesis

A prior that results in a posterior distribution of the same family as the prior

A prior that is independent of the likelihood function

A prior that is derived from the data

A prior that is uniform over all possible hypotheses

It requires more assumptions about the data

It is more computationally expensive

It is less interpretable

It cannot handle large datasets

Maximizing the likelihood of the data

Minimizing the error rate

Updating beliefs based on evidence

Finding the optimal model parameters using gradient descent

It can incorporate prior knowledge into the learning process

It requires fewer assumptions about the data

It is less computationally expensive

It is more interpretable

The probability of the data given a hypothesis

The probability of a hypothesis before observing the data

The probability of a hypothesis given the data

The probability of the data independent of any hypothesis

The probability of the data given a hypothesis

The probability of a hypothesis before observing the data

The probability of a hypothesis given the data

The probability of the data independent of any hypothesis

Central Limit Theorem

Law of Large Numbers

Bayes' Theorem

No Free Lunch Theorem