Taking a sample from a discrete probability distribution. Create the Sample class, which has a function Object() { [native code] } that accepts an array p[] of double values as an argument and supports the following two operations: Return an index I with a probability of p[i]/T (where T is the sum of the numbers in p[]) and change(i, v) to change the value of p[i] to v. Use a complete binary tree with an implied weight of p[i] for each node. Keep the total weight of all the nodes in its subtree in each node. Pick a random number between 0 and T to generate a random index and use the cumulative weights to determine which branch of the subtree to explore. Change the weights of all nodes on the path from the root when updating p[i].

Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Taking a sample from a discrete probability distribution. Create the Sample class, which has a function Object() { [native code] } that accepts an array p[] of double values as an argument and supports the following two operations: Return an index I with a probability of p[i]/T (where T is the sum of the numbers in p[]) and change(i, v) to change the value of p[i] to v. Use a complete binary tree with an implied weight of p[i] for each node. Keep the total weight of all the nodes in its subtree in each node. Pick a random number between 0 and T to generate a random index and use the cumulative weights to determine which branch of the subtree to explore. Change the weights of all nodes on the path from the root when updating p[i].

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