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

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  • Probability of failure with larger number of network elements

    - by MikeKulls
    I'm having a discussion with a work colleague. I'm saying that a network with 100 elements will have pretty much 10 times as many failures as a network with 10 elements, ie a tech will need to replace faulty hardware 10 times more often. He suggests that the failure rate doesn't go up in a linear fashion and the failure rate will be significantly less than 10x, in fact only slightly more failures. This is not the probability of an outage etc, we are just talking in relation to the amount of parts that a tech would need to swap out in a given time frame.

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  • Probability of Blade Chassis Failure

    - by ChrisZZ
    In my organisation we are thinking about buying blade servers - instead of rack servers. Of course technology vendors also make them sound very nice. A concern, that I read very often in different forums, is, that there is a theoretical possibility of the server chassis going down - which would in consequence take all the blades down. That is due to shared infrastructure. My reaction on this probability would be to have redundancy and by two chassis instead of one (very costly of course). Some people (including e.g. HP Vendors) try to convince us, that the chassis is very very unlikely to fail, due to many redundancies (redundant power supply, etc.). Another concern on my side is, that if something goes down, spare parts might be required - which is difficult in our location (Ethiopia). So I would ask to experienced administrators, that have managed blade server: What is your experience? Do they go down as a whole - and what is the sensible shared infrastructure, that might fail? That question could be extended to shared storage. Again I would say, that we need two storage units instead of only one - and again the vendors say, that this things are so rock solid, that no failure is expected. Well - I can hardly believe, that such a critical infrastructure can be very reliable without redundancy - but maybe you can tell me, whether you have successfull blade-based projects, that work without redundancy in its core parts (chassis, storage...) At the moment, we look at HP - as IBM looks much to expensive... thanks a lot best regards Christian

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  • Choose between multiple options with defined probability

    - by Sijin
    I have a scenario where I need to show a different page to a user for the same url based on a probability distribution, so for e.g. for 3 pages the distribution might be page 1 - 30% of all users page 2 - 50% of all users page 3 - 20% of all users When deciding what page to load for a given user, what technique can I use to ensure that the overall distribution matches the above? I am thinking I need a way to choose an object at "random" from a set X { x1, x2....xn } except that instead of all objects being equally likely the probability of an object being selected is defined beforehand.

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  • MySQL create stored procedure fails but all internal queries succeed alone?

    - by Mark
    Hi all, I just created a simple database in MySQL, and I am learning how to write stored proc's. I'm familiar with M$SQL and as far as I can see the following should work: use mydb; -- -------------------------------------------------------------------------------- -- Routine DDL -- -------------------------------------------------------------------------------- DELIMITER // CREATE PROCEDURE mydb.doStats () BEGIN CREATE TABLE IF NOT EXISTS resultprobability ( ballNumber INT NOT NULL , probability FLOAT NULL, PRIMARY KEY (ballNumber) ); CREATE TABLE IF NOT EXISTS drawProbability ( drawDate DATE NOT NULL , ball1 INT NULL , ball2 INT NULL , ball3 INT NULL , ball4 INT NULL , ball5 INT NULL , ball6 INT NULL , ball7 INT NULL , score FLOAT NULL , PRIMARY KEY (drawDate) ); TRUNCATE TABLE resultprobability; TRUNCATE TABLE drawprobability; INSERT INTO resultprobability (ballNumber, probability) (select resultset.ballNumber ballNumber,(count(0)/(select count(0) from resultset)) probability from resultset group by resultset.ballNumber); INSERT INTO drawProbability (drawDate, ball1, ball2, ball3, ball4, ball5, ball6, ball7, score) (select distinct r.drawDate, a.ballnumber ball1, b.ballnumber ball2, c.ballnumber ball3, d.ballnumber ball4, e.ballnumber ball5, f.ballnumber ball6,g.ballnumber ball7, ((a.probability + b.probability + c.probability + d.probability + e.probability + f.probability + g.probability)/7) score from resultset r inner join (select r.drawDate, r.ballNumber, p.probability from resultset r inner join resultprobability p on p.ballNumber = r.ballNumber where r.appearence = 1) a on a.drawdate = r.drawDate inner join (select r.drawDate, r.ballNumber, p.probability from resultset r inner join resultprobability p on p.ballNumber = r.ballNumber where r.appearence = 2) b on b.drawdate = r.drawDate inner join (select r.drawDate, r.ballNumber, p.probability from resultset r inner join resultprobability p on p.ballNumber = r.ballNumber where r.appearence = 3) c on c.drawdate = r.drawDate inner join (select r.drawDate, r.ballNumber, p.probability from resultset r inner join resultprobability p on p.ballNumber = r.ballNumber where r.appearence = 4) d on d.drawdate = r.drawDate inner join (select r.drawDate, r.ballNumber, p.probability from resultset r inner join resultprobability p on p.ballNumber = r.ballNumber where r.appearence = 5) e on e.drawdate = r.drawDate inner join (select r.drawDate, r.ballNumber, p.probability from resultset r inner join resultprobability p on p.ballNumber = r.ballNumber where r.appearence = 6) f on f.drawdate = r.drawDate inner join (select r.drawDate, r.ballNumber, p.probability from resultset r inner join resultprobability p on p.ballNumber = r.ballNumber where r.appearence = 7) g on g.drawdate = r.drawDate order by score desc); END // DELIMITER ; instead i get the following Executed successfully in 0.002 s, 0 rows affected. Line 1, column 1 Error code 1064, SQL state 42000: You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near '' at line 26 Line 6, column 1 Error code 1064, SQL state 42000: You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')) probability from resultset group by resultset.ballNumber); INSERT INTO d' at line 1 Line 31, column 51 Error code 1064, SQL state 42000: You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ') score from resultset r inner join (select r.drawDate, r.ballNumber, p.probabi' at line 1 Line 39, column 114 Execution finished after 0.002 s, 3 error(s) occurred. What am I doing wrong? I seem to have exhausted my limited mental abilities!

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  • banner rotator advertising with probability

    - by cosy
    I have banners advertising with number of views, like CPM system. And for example : i have 3 banner: banner1 with 20.000 nr of views banner2 with 10.000 nr of views banner3 with 5.000 nr of views and on my website the banner must to appear in this position (when the page is reloaded) : banner1 banner2 banner1 banner2 banner3 if the number of views is higher then the probability of apparition is higher how can i do this in php? Thanks a lot for helping :)

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  • Probability algorithm: Finding probable correct item in a list (e.g John, John, Jon)

    - by Andrew White
    Hi, Take for example the list (L): John, John, John, John, Jon We are to presume one item is to be correct (e.g. John in this case), and give a probability it is correct. First (and good!) attempt: MostFrequentItem(L).Count / L.Count (e.g. 4/5 or 80% likelihood) But consider the cases: John, John, Jon, Jonny John, John, Jon, Jon I want to consider the likelihood of the correct item being John to be higher in the first list! I know I have to count the SecondMostFrequent Item and compare them. Any ideas? This is really busting my brain! Thx, Andrew

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  • Discrete mathematics problem - Probability theory and counting

    - by Mohammad
    Hello All, I'm taking a discrete mathematics course, and I encountered a question and I need your help. I don't know if this is the right place for that though :) It says: Each user on a computer system has a password, which is six to eight characters long, where each character is an uppercase letter or a digit. Each password must contain at least one digit. How many possible passwords are there? The book solves this by adding the probabilities of having six,seven and eight characters long password. However, when he solves for probability of six characters he does this P6 = 36^6 - 26^6 and does P7 = 36^7 - 26^7 and P8 = 36^8 - 26^8 and then add them all I understand the solution, but my question is why doesn't calculating, P6 = 10*36^5 and the same for P7 and P8, work? 10 for the digit and 36 for the alphanumeric? Also, if anyone could give me another solution, other than the one in the book. Thank you very much :)

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  • Reinforcement learning And POMDP

    - by Betamoo
    I am trying to use Multi-Layer NN to implement probability function in Partially Observable Markov Process.. I thought inputs to the NN would be: current state, selected action, result state; The output is a probability in [0,1] (prob. that performing selected action on current state will lead to result state) In training, I fed the inputs stated before, into the NN, and I taught it the output=1.0 for each case that already occurred. The problem : For nearly all test case the output probability is near 0.95.. no output was under 0.9 ! Even for nearly impossible results, it gave that high prob. PS:I think this is because I taught it happened cases only, but not un-happened ones.. But I can not at each step in the episode teach it the output=0.0 for every un-happened action! Any suggestions how to over come this problem? Or may be another way to use NN or to implement prob function? Thanks

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  • Determining the chances of an event occurring when it hasn't occurred yet

    - by sanity
    A user visits my website at time t, and they may or may not click on a particular link I care about, if they do I record the fact that they clicked the link, and also the duration since t that they clicked it, call this d. I need an algorithm that allows me to create a class like this: class ClickProbabilityEstimate { public void reportImpression(long id); public void reportClick(long id); public double estimateClickProbability(long id); } Every impression gets a unique id, and this is used when reporting a click to indicate which impression the click belongs to. I need an algorithm that will return a probability, based on how much time has past since an impression was reported, that the impression will receive a click, based on how long previous clicks required. Clearly one would expect that this probability will decrease over time if there is still no click. If necessary, we can set an upper-bound, beyond which we consider the click probability to be 0 (eg. if its been an hour since the impression occurred, we can be pretty sure there won't be a click). The algorithm should be both space and time efficient, and hopefully make as few assumptions as possible, while being elegant. Ease of implementation would also be nice. Any ideas?

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  • Probability Question

    - by Juddling
    if i pick 10 numbers from a possible 80. what is the probablity for someone else picking the same 10 numbers as me? or just 4 numbers? or no numbers? i think it's no matches: 10/80 one match: 10*9/80*79 so the formula would be (10!/matches!)/(80!/matches!) is this right? i've only just started doing this at A-level and i need it for a game script i'm making.

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  • C++: Calculate probability percentage during each iteration

    - by Mur Quirk
    Can't seem to get this to work. The idea is to calculate the percentage of heads and tails after each count, accumulating after each iteration. Except I keep getting nan% for my calculations. Anybody see what I'm doing wrong? void flipCoin(time_t seconds, int flipCount){ vector<int> flips; float headCount = 0; float tailCount = 0; double headProbability = double((headCount/(headCount + tailCount))*100); double tailProbability = double((tailCount/(headCount + tailCount))*100); for (int i=0; i < flipCount; i++) { int flip = rand() % (HEADS - TAILS + 1) + TAILS; flips.push_back(flip); if (flips[i] == 1) { tailCount++; cout << "Tail Percent: " << tailProbability << "%" << endl; }else{ headCount++; cout << "Head Percent: " << headProbability << "%" << endl; } } }

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  • Where to find viterbi algorithm transition values for natural language processing?

    - by Rodrigo Salazar
    I just watched a video where they used Viterbi algorithm to determine whether certain words in a sentence are intended to be nouns/verbs/adjs etc, they used transition and emission probabilities, for example the probability of the word 'Time' being used as a verb is known (emission) and the probability of a noun leading onto a verb (transition). http://www.youtube.com/watch?v=O_q82UMtjoM&feature=relmfu (The video) How can I find a good dataset of transition and emission probabilities for this use-case? Or EVEN just a single example with all the probabilities displayed, I want to use realistic numbers in a demonstration.

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  • Calculating odds distribution with 6-sided dice

    - by Stephen
    I'm trying to calculate the odds distribution of a changing number of 6-sided die rolls. For example, 3d6 ranges from 3 to 18 as follows: 3:1, 4:3, 5:6, 6:10, 7:15, 8:21, 9:25, 10:27, 11:27, 12:25, 13:21, 14:15, 15:10, 16:6, 17:3, 18:1 I wrote this php program to calculate it: function distributionCalc($numberDice,$sides=6) { for ( $i=0; $i<pow($sides,$numberDice); $i++) { $sum=0; for ($j=0; $j<$numberDice; $j++) { $sum+=(1+(floor($i/pow($sides,$j))) % $sides); } $distribution[$sum]++; } return $distribution; } The inner $j for-loop uses the magic of the floor and modulus functions to create a base-6 counting sequence with the number of digits being the number of dice, so 3d6 would count as: 111,112,113,114,115,116,121,122,123,124,125,126,131,etc. The function takes the sum of each, so it would read as: 3,4,5,6,7,8,4,5,6,7,8,9,5,etc. It plows through all 3^6 possible results and adds 1 to the corresponding slot in the $distribution array between 3 and 18. Pretty straightforward. However, it only works until about 8d6, afterward i get server time-outs because it's now doing billions of calculations. But I don't think it's necessary because die probability follows a sweet bell-curve distribution. I'm wondering if there's a way to skip the number crunching and go straight to the curve itself. Is there a way to do this, so, for example, with 80d6 (80-480)? Can the distribution be projected without doing 6^80 calculations? I'm not a professional coder and probability is still new to me, so thanks for all the help! Stephen

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  • problem while removing an element from the TreeSet

    - by harshit
    I am doing the following class RuleObject implements Comparable{ @Override public String toString() { return "RuleObject [colIndex=" + colIndex + ", probability=" + probability + ", rowIndex=" + rowIndex + ", rule=" + rule + "]"; } String rule; double probability; int rowIndex; int colIndex; public RuleObject(String rule, double probability) { this.rule = rule; this.probability = probability; } @Override public int compareTo(Object o) { RuleObject ruleObj = (RuleObject)o; System.out.println(ruleObj); System.out.println("---------------"); System.out.println(this); if(ruleObj.probability > probability) return 1; else if(ruleObj.probability < probability) return -1; else{ if(ruleObj.colIndex == this.colIndex && ruleObj.rowIndex == this.rowIndex && ruleObj.probability == this.probability && ruleObj.rule.equals(this.rule)) return 0; } return 1; } } And I have a TreeSet containing elements of RuleObject. I am trying to do the following : System.out.println(sortedHeap.size()); RuleObject ruleObj = sortedHeap.first(); sortedHeap.remove(ruleObj); System.out.println(sortedHeap.size()); I can see that the size of set remains same. I am not able to understand why is it not being deleted. Also while deleting I could see compareTo method is called. But it is called for only 3 object whereas in set there are 8 objects. Thanks

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  • What's the proper way to calculate probability for a card game?

    - by Milan Babuškov
    I'm creating AI for a card game, and I run into problem calculating the probability of passing/failing the hand when AI needs to start the hand. Cards are A, K, Q, J, 10, 9, 8, 7 (with A being the strongest) and AI needs to play to not take the hand. Assuming there are 4 cards of the suit left in the game and one is in AI's hand, I need to calculate probability that one of the other players would take the hand. Here's an example: AI player has: J Other 2 players have: A, K, 7 If a single opponent has AK7 then AI would lose. However, if one of the players has A or K without 7, AI would survive. Now, looking at possible distribution, I have: P1 P2 AI --- --- --- AK7 loses AK 7 survives A7 K survives K7 A survives A 7K survives K 7A survives 7 KA survives AK7 loses Looking at this, it seems that there is 75% chance of survival. However, I skipped the permutations that mirror the ones from above. It should be the same, but somehow when I write them all down, it seems that chance is only 50%: P1 P2 AI --- --- --- AK7 loses A7K loses K7A loses KA7 loses 7AK loses 7KA loses AK 7 survives A7 K survives K7 A survives KA 7 survives 7A K survives 7K A survives A K7 survives A 7K survives K 7A survives K A7 survives 7 AK survives 7 KA survives AK7 loses A7K loses K7A loses KA7 loses 7AK loses 7KA loses 12 loses, 12 survivals = 50% chance. Obviously, it should be the same (shouldn't it?) and I'm missing something in one of the ways to calculate. Which one is correct?

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  • How to generate correlated binary variables

    - by jonalm
    Dear All I need to generate a series of N random binary variables with a given correlation function. Let x = {x_i} be a series of binary variables (taking the value 0 or 1, i running form 1 to N). The marginal probability is given Pr(x_i = 1) = p, and the values should be correlated in the following way E[ x_i x_j ] = const * |i-j|^-alfa where alfa is a positive number. Is it possible to generate a series like this? preferably in python.

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  • Weighted random selection using Walker's Alias Method (c# implementation)

    - by Chuck Norris
    I was looking for this algorithm (algorithm which will randomly select from a list of elements where each element has different probability of being picked (weight) ) and found only python and c implementations, after I did a C# one, a bit different (but I think simpler) I thought I should share it, and ask your opinion ? this is it: using System; using System.Collections.Generic; using System.Linq; namespace ChuckNorris { class Program { static void Main(string[] args) { var oo = new Dictionary<string, int> { {"A",7}, {"B",1}, {"C",9}, {"D",8}, {"E",11}, }; var rnd = new Random(); var pick = rnd.Next(oo.Values.Sum()); var sum = 0; var res = ""; foreach (var o in oo) { sum += o.Value; if(sum >= pick) { res = o.Key; break; } } Console.WriteLine("result is "+ res); } } } if anyone can remake it in f# please post your code

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  • What is the best Java numerical method package?

    - by Bob Cross
    I am looking for a Java-based numerical method package that provides functionality including: Solving systems of equations using different numerical analysis algorithms. Matrix methods (e.g., inversion). Spline approximations. Probability distributions and statistical methods. In this case, "best" is defined as a package with a mature and usable API, solid performance and numerical accuracy. Edit: derick van brought up a good point in that cost is a factor. I am heavily biased in favor of free packages but others may have a different emphasis.

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  • Reinforcement learning toy project

    - by Betamoo
    My toy project to learn & apply Reinforcement Learning is: - An agent tries to reach a goal state "safely" & "quickly".... - But there are projectiles and rockets that are launched upon the agent in the way. - The agent can determine rockets position -with some noise- only if they are "near" - The agent then must learn to avoid crashing into these rockets.. - The agent has -rechargable with time- fuel which is consumed in agent motion - Continuous Actions: Accelerating forward - Turning with angle I need some hints and names of RL algorithms that suit that case.. - I think it is POMDP , but can I model it as MDP and just ignore noise? - In case POMDP, What is the recommended way for evaluating probability? - Which is better to use in this case: Value functions or Policy Iterations? - Can I use NN to model environment dynamics instead of using explicit equations? - If yes, Is there a specific type/model of NN to be recommended? - I think Actions must be discretized, right? I know it will take time and effort to learn such a topic, but I am eager to.. You may answer some of the questions if you can not answer all... Thanks

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  • PCA extended face recognition

    - by cMinor
    The state of the art says that we can use PCA to perform face recognition. like this, this or this I am working with a project that involves training a classifier to detect a person who is wearing glasess or hats or even a mustache. The purpose of doing this is to detect when a person that has robbed a bank, store, or have commeted some sort of crime(s) (we have their image in a database), enters a certain place ( historically we know these guys have robbed, so we should take care to avoid problems). We came first to have a distributed database with all images of criminals, then I thought to have a layer of them clasifying these criminals using accesories like hats, mustache or anything that hides their face etc... Then, to apply that knowledge to detect when a particular or a suspect person enters a comercial place. ( In practice when someone is going to rob not all the times they are using an accesorie...) What do you think about this idea of doing PCA to first detect principal components of the face and then the components of an accesory. I was thinking that maybe a probabilistic approach is better so we can compute the probability the criminal is the person that entered a place and call the respective authorities.

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  • The mathematics of Schellings segregation model

    - by Bruce
    For those who don't know the model. You can read this pdf. I want to find what is the probability that 2 nodes are each others neighbors when the algorithm converges (i.e. when all nodes are happy). Here's the model in a gist. You have a grid (say 10x10). You have nodes of two kind (red and green) 45 each. So we have 10 empty spaces. We randomly place the nodes on the grid. Now we scan through this grid (Exact order does not matter according to Schelling). Each node wants a specific percentage of people of same kind in its Moore neighborhood (say b = 50% for each red and green). We calculate the happiness of each node (a = Number of neighbors of same kind/Number of neighbors of different kind). If a node is unhappy (a < b) it moves to an empty cell where it knows it will be happy. This movement can change the dynamics of old as well as new neighborhood. Algorithm converges when all nodes are happy. PS - I am looking for links for any mathematical analysis of the Schelling's model.

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  • Fitting Gaussian KDE in numpy/scipy in Python

    - by user248237
    I am fitting a Gaussian kernel density estimator to a variable that is the difference of two vectors, called "diff", as follows: gaussian_kde_covfact(diff, smoothing_param) -- where gaussian_kde_covfact is defined as: class gaussian_kde_covfact(stats.gaussian_kde): def __init__(self, dataset, covfact = 'scotts'): self.covfact = covfact scipy.stats.gaussian_kde.__init__(self, dataset) def _compute_covariance_(self): '''not used''' self.inv_cov = np.linalg.inv(self.covariance) self._norm_factor = sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.n def covariance_factor(self): if self.covfact in ['sc', 'scotts']: return self.scotts_factor() if self.covfact in ['si', 'silverman']: return self.silverman_factor() elif self.covfact: return float(self.covfact) else: raise ValueError, \ 'covariance factor has to be scotts, silverman or a number' def reset_covfact(self, covfact): self.covfact = covfact self.covariance_factor() self._compute_covariance() This works, but there is an edge case where the diff is a vector of all 0s. In that case, I get the error: File "/srv/pkg/python/python-packages/python26/scipy/scipy-0.7.1/lib/python2.6/site-packages/scipy/stats/kde.py", line 334, in _compute_covariance self.inv_cov = linalg.inv(self.covariance) File "/srv/pkg/python/python-packages/python26/scipy/scipy-0.7.1/lib/python2.6/site-packages/scipy/linalg/basic.py", line 382, in inv if info>0: raise LinAlgError, "singular matrix" numpy.linalg.linalg.LinAlgError: singular matrix What's a way to get around this? In this case, I'd like it to return a density that's essentially peaked completely at a difference of 0, with no mass everywhere else. thanks.

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