What is the EM algorithm used to optimize in the "M" step?Select one:a.The latent variablesb.The model parametersc.The prediction accuracy of the modeld.The likelihood of the model

What is the main goal of the EM algorithm?Select one:a.To maximize the prediction accuracy of the modelb.To minimize the cost or loss function of a modelc.To minimize the error between the predicted and actual values of the datad.To maximize the likelihood of a model given the data

What purpose does the Expectation-Maximisation (EM) algorithm serve in the Gaussian Mixture Model (GMM)?Updating the Gaussian parameters to best fit the data.Calculating the probability density function of the data.Initialising the parameters of the Gaussian components.Assigning data points to clusters based on their likelihood.

Consider an observed random sample of size n, w1, . . . , wn, from a normal distribution N(µ, σ2 ). To the 75 observations in the dataset Data-A1a.csv apply the EM algorithm to fit via maximum likelihood the two-component normal mixture density with common variances, Write down the expressions for the E- and M-steps. on the (k + 1) th iteration of the EMalgorithm.

Write down the expressions for the E- and M-steps. on the (k + 1)th iteration of the EM algorithm.

True or false: The Expectation Maximisation (EM) algorithm in a Gaussian mixture model (GMM) is employed not only to derive the maximum likelihood estimates of the model parameters but also to ascertain the number of clusters that maximise the data likelihood.TrueFalse