1, 2, 5, 10. The probability of choosing a(ny) 1 from the set is 0.17, the probability of choosing a(ny) 2 from the set is 0.24, The probability of choosing a(ny) 5 from the set is 0.18. What is the probability of choosing a(ny) 10 from the set? Your answer can only be a probability, which is a real number (including decimal point) between 0 and 1, and no other characters, punctuation, le

1, 2, 5, 10. The probability of choosing a(ny) 1 from the set is 0.17, the probability of choosing a(ny) 2 from the set is 0.24, The probability of choosing a(ny) 5 from the set is 0.18. What is the probability of choosing a(ny) 10 from the set? Your answer can only be a probability, which is a real number (including decimal point) between 0 and 1, and no other characters, punctuation, le

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter19: Probabilistic Dynamic Programming

Section19.4: Further Examples Of Probabilistic Dynamic Programming Formulations

Problem 7P

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Bus timetables specify to the second the exact arrival and departure time of each bus on each stop. You need to pay for the full fare of every bus you ride and different bus lines charge different fees , but they are flat fees (independent of distance travelled on the line) A travel plan is a sequence of stop-time pairs where stop is a location of a bus stop and time is when we arrive at that stop. The plan is feasible if for any two consecutive pairs (a, t) and (b, t′) in the plan there exists a bus that departs after t and arrives at b at exactly t′. That is, a travel plan does not allow us to walk between stops. Assuming that no two buses arrive at the same time at the same stop, a feasible plan uniquely identifies the bus lines that we need to take to realize the plan. The cost of the plan is the sum of the fares we need to pay. Your task is to design an efficient algorithm that given a departure time t, an arrival time t′, an origin stop a and a destination stop b, finds the…
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6. The Jaccard coefficient between two sets {a, b, c, d, e, f, g} and {a, b, g, h, i, j} is: Group of answer choices 0.2 0.3 0.4 0.5
Step 1. Intersection over Union # def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy() # predictions dt_bboxes =…
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In a candy store, there are N different types of candies available and the prices of all the N different types of candies are provided to you. You are now provided with an attractive offer. You can buy a single candy from the store and get at most K other candies ( all are different types ) for free. Now you have to answer two questions. Firstly, you have to find what is the minimum amount of money you have to spend to buy all the N different candies. Secondly, you have to find what is the maximum amount of money you have to spend to buy all the N different candies. In both the cases you must utilize the offer i.e. you buy one candy and get K other candies for free. Example 1: Input: N = 4 K = 2 %3D candies[] = {3 2 1 4} Output: 3 7
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