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problem1 write a non-recursive program to print out the keys from a binary search tree in order2 draw the binary search
problem1 suppose that we have an estimate ahead of time of how often search keys are to be accessed in a binary tree
problem1 suppose that ai 2i for 1 le i le n how many table positions are examined by interpolation search during the
problem1 implement a sequential searching algorithm which averages about n2 steps for both successful and unsuccessful
problem1 how would you sort the contents of a disk if no other storage except main memory were available for use2 how
problem1 describe how you would do external selection find the kth largest element in a file of n elements where n is
problem1 true or false the running time of merge sort does not depend on the value of the keys in the input file
problem1 implement a recursive merge sort with a cutoff to insertion sort for sub files with less than m elements
problem1 what is the minimum number of keys that must be moved during a remove the largest operation in a heap draw a
problem1 which positions could be occupied by the 3rd largest key in a heap of size 32 which positions could not be
problem1 draw the heap that results when the following operations are performed on an initially empty heap insert 1
problem1 aside from the extra memory requirement what is the major disadvantage of the strategy of doing straight radix
problem1 compare the number of exchanges used by radix-exchange sort with the number used by quicksort for the file 001
problem1 what is the maximum number of times during the execution of quicksort that the largest element can be moved2
problemthe value of perfect information measures the change in our meu if we allow observing a variable that was not
problemconsider the problem of computing the optimal action for an agent whose utility function we are uncertain about
problemconsider the problem of bayesian learning for a functional causal model c over a set of endogenous variables x
problem1 consider a particular parameterization theta eta to max-margin show how we can use second-best map inference
problemin this exercise we show how to learn markov networks with shared parameters such as a relational markov network
problemwe now consider how to use the interpretation of the em as maximizing an energy functional to allow partial or
problemsuppose that we have an incomplete data set d and network structure g and matching parameters moreover suppose
problemconsider learning the parameters of the network h rarr x h rarr y where h is a hidden variable show that the
problema consider the task of estimating the parameters of a univariate gaussian distribution n micro sigma2nbsp from a
problemconsider the problem of applying em to parameter estimation for a variable x whose local probabilistic model is