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Search found 309 results on 13 pages for 'probability'.

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• Discrete problem of probability theory [closed]

  - by calejero

  A jury consists of 12 persons each of which has, before the trial started, a probability of 0.4 to vote in favor of the defendant's innocence. During the trial, the lawyer has a probability of 0.6 to change the mind of each juror who was biased against the accused. How likely is the defendant to be acquitted if he needs 10 votes in favor?

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• Probability Homework Help

  - by Alon

  A child is tasting two types of chocolate. The probability that he will like the first type if he liked the second type is 5/6. The probability that he will like the second type if he liked the first type is 3/8. It is known that the probability that the child won't like the second type of chocolate is doubled than the probability that he won't like the first type. What is the probability that the child like at least one of the two type of chocolates? So I tried: 1-[P(not like the first type AND not like the second type)] which is like: 1-[P(not like the first type)*P(not like first type / not like the second type) + P(not like the second type) which equals: P(not like first type / not like second type) But now, I don't have the data of the conditional probability. In addition, I'd like to see how could it solved using a computer programming language. Any ideas? Thank you.

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• C++ function for picking from a list where each element has a distinct probability

  - by Stuart

  I have an array of structs and one of the fields in the struct is a float. I want to pick one of the structs where the probability of picking it is relative to the value of the float. ie struct s{ float probability; ... } sArray s[50]; What is the fastest way to decide which s to pick? Is there a function for this? If I knew the sum of all the probability fields (Note it will not be 1), then could I iterate through each s and compare probability/total_probability with a random number, changing the random number for each s? ie if( (float) (rand() / RAND_MAX) < probability)...

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• randomized quicksort: probability of two elements comparison?

  - by bantu

  I am reading "Probability and Computing" by M.Mitzenmacher, E.Upfal and I have problems understanding how the probability of comparison of two elements is calculated. Input: the list (y1,y2,...,YN) of numbers. We are looking for pivot element. Question: what is probability that two elements yi and yj (ji) will be compared? Answer (from book): yi and yj will be compared if either yi or yj will be selected as pivot in first draw from sequence (yi,yi+1,...,yj-1,yj). So the probablity is: 2/(y-i+1). The problem for me is initial claim: for example, picking up yi in the first draw from the whole list will cause the comparison with yj (and vice-versa) and the probability is 2/n. So, rather the "reverse" claim is true -- none of the (yi+1,...,yj-1) elements can be selected beforeyi or yj, but the "pool" size is not fixed (in first draw it is n for sure, but on the second it is smaller). Could someone please explain this, how the authors come up with such simplified conclusion? Thank you in advance

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• Function calculating the probability of a letter in an sentence

  - by Mike

  I have a function that is supposed to calculate the number of times a letter occurs in a sentence, and based on that, calculate the probability of it occurring in the sentence. To accomplish this, I have a sentence: The Washington Metropolitan Area is the most educated and affluent metropolitan area in the United States. An array of structures, containing the letter, the number of times it occurs, and the probability of it occurring, with one structure for each letter character and an additional structure for punctuation and spaces: struct letters { char letter; int occur; double prob; }box[53]; This is the function itself: void probability(letters box[53], int sum { cout<<sum<<endl<<endl; for(int c8=0;c8<26;c8++) { box[c8].prob = (box[c8].occur/sum); cout<<box[c8].letter<<endl; cout<<box[c8].occur<<endl; cout<<box[c8].prob<<endl<<endl; } } It correctly identifies that there are 90 letters in the sentence in the first line, prints out the uppercase letter as per the structure in the second line of the for loop, and prints out the number of times it occurs. It continually prints 0 for the probability. What am I doing wrong?

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• How do I apply different probability factors in an algorithm for a cricket simulation game?

  - by Komal Sharma

  I am trying to write the algorithm for a cricket simulation game which generates runs on each ball between 0 to 6. The run rate or runs generated changes when these factors come into play like skill of the batsman, skill of the bowler, target to be chased. Wickets left. If the batsman is skilled more runs will be generated. There will be a mode of play of the batsman aggressive, normal, defensive. If he plays aggressive chances of getting out will be more. If the chasing target is more the run rate should be more. If the overs are final the run rate should be more. I am using java random number function for this. The code so far I've written is public class Cricket { public static void main(String args[]) { int totalRuns=0; //i is the balls bowled for (int i = 1; i <= 60 ; i++) { int RunsPerBall = (int)(Math.random()*6); //System.out.println(Random); totalRuns=totalRuns+RunsPerBall; } System.out.println(totalRuns); } } Can somebody help me how to apply the factors in the code. I believe probability will be used with this. I am not clear how to apply the probability of the factors stated above in the code.

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• Select random row from MySQL (with probability)

  - by James Simpson

  I have a MySQL table that has a row called cur_odds which is a percent number with the percent probability that that row will get selected. How do I make a query that will actually select the rows in approximately that frequency when you run through 100 queries for example? I tried the following, but a row that has a probability of 0.35 ends up getting selected around 60-70% of the time. SELECT * FROM table ORDER BY RAND()*cur_odds DESC

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• Probability Random Number Generator

  - by Excl

  Let's say I'm writing a simple luck game - each player presses Enter and the game assign him a random number between 1-6. Just like a cube. At the end of the game, the player with the highest number wins. Now, let's say I'm a cheater. I want to write the game so player #1 (which will be me) has a probability of 90% to get six, and 2% to get each of the rest numbers (1, 2, 3, 4, 5). So, how can I generate a number random, and set the probability for each number? Thanks.

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• Probability Selector

  For when you really do need randomness Probability - Math - Event - Teaching Resources - Past Events

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• Probability and Neural Networks

  - by Betamoo

  Is it a good practice to use sigmoid or tanh output layers in Neural networks directly to estimate probabilities? i.e the probability of given input is the output of sigmoid function in the NN

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• probability and relative frequency

  - by Alexandru

  If I use relative frequency to estimate the probability of an event, how good is my estimate based on the number of experiments? Is standard deviation a good measure? A paper/link/online book would be perfect. http://en.wikipedia.org/wiki/Frequentist

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• probability of subset sum after rolling dice 4 times

  - by binger

  If we roll 4 dices (fair), what is the probability of "sum of subset" being 5. e.g. 1432,1121, 2344, 2354 have a subset sum of 5. Can you illustrate how to calculate this.

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• Combinatorics, probability, dice

  - by TarGz

  A friend of mine asked: if I have two dice and I throw both of them, what is the most frequent sum (of the two dice' numbers)? I wrote a small script: from random import randrange d = dict((i, 0) for i in range(2, 13)) for i in xrange(100000): d[randrange(1, 7) + randrange(1, 7)] += 1 print d Which prints: 2: 2770, 3: 5547, 4: 8379, 5: 10972, 6: 13911, 7: 16610, 8: 14010, 9: 11138, 10: 8372, 11: 5545, 12: 2746 The question I have, why is 11 more frequent than 12? In both cases there is only one way (or two, if you count reverse too) how to get such sum (5 + 6, 6 + 6), so I expected the same probability..?

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• Probability distribution for sms answer delays

  - by Thomas Ahle

  I'm writing an app using sms as communication. I have chosen to subscribe to an sms-gateway, which provides me with an API for doing so. The API has functions for sending as well as pulling new messages. It does however not have any kind of push functionality. In order to do my queries most efficient, I'm seeking data on how long time people wait before they answer a text message - as a probability function. Extra info: The application is interactive (as can be), so I suppose the times will be pretty similar to real life human-human communication. I don't believe differences in personal style will play a big impact on the right times and frequencies to query, so average data should be fine.

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• Logic / Probability Question: Picking from a bag

  - by Rosarch

  I'm coding a board game where there is a bag of possible pieces. Each turn, players remove randomly selected pieces from the bag according to certain rules. For my implementation, it may be easier to divide up the bag initially into pools for one or more players. These pools would be randomly selected, but now different players would be picking from different bags. Is this any different? If one player's bag ran out, more would be randomly shuffled into it from the general stockpile.

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• Calculating spam probability in python

  - by Hobhouse

  I am building a website in python/django and want to predict wether a user submission is valid or wether it is spam. Users have an accept rate on their submissions, like this website has. Users can moderate other users' submissions; and these moderations are later metamoderated by an admin. Given this: user A with an submission accept rate of 60% submits something. user B moderates A's post as a valid submission. However, his moderations are often wrong, and his moderations' accept rate is a mere 30%. user C moderates A's post as spam. User C is usually right. His moderations' accept rate is 80%. How can I predict the chance of A's post being spam?

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• Calculating spam probability

  - by Hobhouse

  I am building a website in python/django and want to predict wether a user submission is valid or wether it is spam. Users have an accept rate on their submissions, like this website has. Users can moderate other users' submissions; and these moderations are later metamoderated by an admin. Given this: user A with an submission accept rate of 60% submits something. user B moderates A's post as a valid submission. However, his moderations are often wrong, and his moderations' accept rate is a mere 30%. user C moderates A's post as spam. User C is usually right. His moderations' accept rate is 80%. How can I predict the chance of A's post being spam?

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• Computationally simple Pseudo-Gaussian Distribution with varying mean and standard deviation?

  - by mstksg

  This picture from wikipedia has a nice example of the sort of functions I'd ideally like to generate http://en.wikipedia.org/wiki/File:Normal_Distribution_PDF.svg Right now I'm using the Irwin-Hall Distribution, which is more or less a Polynomial approximation of the Gaussian distribution...basically, you use uniform random number generator and iterate it x times, and take the average. The more iterations, the more like a Gaussian Distribution it is. It's pretty nice; however I'd like to be able to have one where I can vary the mean. For example, let's say I wanted a number between the range 0 and 10, but around 7. Like, the mean (if I repeated this function multiple times) would turn out to be 7, but the actual range is 0-10. Is there one I should look up, or should I work on doing some fancy maths with standard Gaussian Distributions?

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• Formula to calculate probability of unrecoverable read error during RAID rebuild

  - by OlafM

  I need to compare the reliability of different RAID systems with either consumer or enterprise drives. The formula to have the probability of success of a rebuild, ignoring mechanical problems, is simple: error_probability = 1 - (1-per_bit_error_rate)^bit_read and with 3 TB drives I get 38% probability to experience an URE (unrecoverable read error) for a 2+1 disks RAID5 (4.7% for enterprise drives) 21% for a RAID1 (2.4% for enterprise drives) 51% probability of error during recovery for the 3+1 RAID5 often used by users of SOHO products like Synologys. Most people don't know about this. Calculating the error for single disk tolerance is easy, my question concerns systems tolerant to multiple disks failures (RAID6/Z2, RAIDZ3 and RAID1 with multiple disks). If only the first disk is used for rebuild and the second one is read again from the beginning in case or an URE, then the error probability is the one calculated above squared (14.5% for consumer RAID5 2+1, 4.5% for consumer RAID1 1+2). However, I suppose (at least in ZFS that has full checksums!) that the second parity/available disk is read only where needed, meaning that only few sectors are needed: how many UREs can possibly happen in the first disk? not many, otherwise the error probability for single-disk tolerance systems would skyrocket even more than I calculated. If I'm correct, a second parity disk would practically lower the risk to extremely low values. Am I correct?

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• How to create reproducible probability in map generation?

  - by nickbadal

  So for my game, I'm using perlin noise to generate regions of my map (water/land, forest/grass) but I'd also like to create some probability based generation too. For instance: if(nextInt(10) > 2 && tile.adjacentTo(Type.WATER)) tile.setType(Type.SAND); This works fine, and is even reproduceable (based on a common seed) if the nextInt() calls are always in the same order. The issue is that in my game, the world is generated on demand, based on the player's location. This means, that if I explore the map differently, and the chunks of the map are generated in a different order, the randomness is no longer consistent. How can I get this sort of randomness to be consistent, independent of call order? Thanks in advance :)

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• In terms of loss of volume or corruption, is failure probability of an Amazon EBS volume 'x', indepe

  - by Tony Morgan

  In terms of loss of volume or corruption, is failure probability of an Amazon EBS* volume 'x', independent of the failure of another volume 'y'. Amazon states[1] AFR** of between 0.1%-0.5%, lets say 0.5%, 0.005. To restate the question is the AFR composed of two EBSs mirrored actually 0.005*0.005 = 0.000025? To be clear I'm not interested in high availability here, just very high durability. *EBS = elastic block storage (amazons persistant disks) **AFR = annual failure rate. [1] http://aws.amazon.com/ebs/

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• How to know the exactly probability of a multi-class prediction using libsvm?

  - by lgnlgn

  Using svm_predict_probability to estimate the probabilty for a multi-class. I need to know what the order of the returned. It does not seem to be in increasing or decreasing order of the class labels, or the array of labels returned by svm_get_labels. So how do I know which class is coresponding to a probability? Many thanks

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• probability of trouble-free upgrade

  - by intuited

  One of the problems with recommending Ubuntu to potential future users, especially those not particularly given to technical endeavours, is that there is a chance that upgrades will break their machine, and they'll have to pay or otherwise coerce some knowledgeable person into fixing them. In my limited experience of running successive versions of Ubuntu since 8-something on a couple of different laptops, this chance is quite high. I'm not sure if I'm just unlucky with the hardware that I'm using, or if it's a result of the higher-than-average number of packages I have installed, or if upgrades are just typically problematic. So I'd like to know the likelihood, for a casual user, of doing a release upgrade, for example from 10.04 to 10.10, without experiencing any regression bugs. Obviously this is dependent on the hardware that people are running. Canonical seems to be making some efforts towards collecting data on this, for example with the "I am affected by this bug" checkbox on their issue tracker, and with the laptop compatibility reports, but I've not seen anything comprehensive. I'm hoping for an objective reference here, for example a study carried out by relatively unbiased individuals. However, anecdotal evidence is probably useful too.

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• reservoir sampling problem: correctness of proof

  - by eSKay

  This MSDN article proves the correctness of Reservoir Sampling algorithm as follows: Base case is trivial. For the k+1st case, the probability a given element i with position <= k is in R is s/k. The probability i is replaced is the probability k+1st element is chosen multiplied by i being chosen to be replaced, which is: s/(k+1) * 1/s = 1/(k+1), and prob that i is not replaced is k/k+1. So any given element's probability of lasting after k+1 rounds is: (chosen in k steps, and not removed in k steps) = s/k * k/(k+1), which is s/(k+1). So, when k+1 = n, any element is present with probability s/n. about step 3: What are the k+1 rounds mentioned? What is chosen in k steps, and not removed in k steps? Why are we only calculating this probability for elements that were already in R after the first s steps?

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• reservoir sampling problem

  - by eSKay

  This MSDN article proves the correctness of Reservoir Sampling algorithm as follows: Base case is trivial. For the k+1st case, the probability a given element i with position <= k is in R is s/k. The probability i is replaced is the probability k+1st element is chosen multiplied by i being chosen to be replaced, which is: s/(k+1) * 1/s = 1/(k+1), and prob that i is not replaced is k/k+1. So any given element's probability of lasting after k+1 rounds is: (chosen in k steps, and not removed in k steps) = s/k * k/(k+1), which is s/(k+1). So, when k+1 = n, any element is present with probability s/n. about step 3: What are the k+1 rounds mentioned? What is chosen in k steps, and not removed in k steps? Why are we only calculating this probability for elements that were already in R after the first s steps?

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