# Question

Consider the application of EM to learn the parameters for the network in Figure (a), given the true parameters in Equation (20.7).

a. Explain why the EM algorithm would not work if there were just two attributes in the model rather than three.

b. Show the calculations for the first iteration of EM starting from Equation (20.8).

c. What happens if we start with all the parameters set to the same value p?

d. Write out an expression for the log likelihood of the tabulated candy data in terms of the parameters, calculate the partial derivatives with respect to each parameter, and investigate the nature of the fixed point reached in part(c)

a. Explain why the EM algorithm would not work if there were just two attributes in the model rather than three.

b. Show the calculations for the first iteration of EM starting from Equation (20.8).

c. What happens if we start with all the parameters set to the same value p?

d. Write out an expression for the log likelihood of the tabulated candy data in terms of the parameters, calculate the partial derivatives with respect to each parameter, and investigate the nature of the fixed point reached in part(c)

## Answer to relevant Questions

Construct by hand a neural network that computes the XOR function of two inputs. Make sure to specify what sort of units you are using.Starting from Equation (20.13), show that δ I, / δ W j = Err x a j.Starting with the passive ADP agent modify it to use an approximate ADP algorithm us discussed in the text. Do this in two steps:a. Implement a priority queue for adjustments to the utility estimates. Whenever a state is ...Investigate the application of reinforcement learning ideas to the modeling of human and animal behavior.For each of the preceding three grammars, write down three sentences of English and three sentences of non-English generated by the grammar. Each sentence should be significantly different, should be at least six words long, ...Post your question

0