This is a biased coin. The probability of a head is 34.It is flipped 60 times.How many heads do you expect?33/4*6()=45 相关知识点: 试题来源: 解析 http://gallery.fbcontent.cn/latex?decode=false&latex=%24%24%7B%7B3%7D%5Cover%7B4%7D%7D%20%24%24%25&fontSize=68http://galler...
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A coin is biased so that the head is 3 times as likely to occuras tail. If the coin is tossed twice, find the probability distribution of number of tails .
【题目】A coin is beleved to be biased. Alison and Paul want to test the coin to see if the probability oft landing on heads p i signifcantly less than. They both use a 2% significance level.Alison spins the coin 30times and record the number of heads.Alison and Paul both observe ...
Finding a most biased coin with fewest flipsKarthekeyan ChandrasekaranRichard KarpMicrotome PublishingConference on Learning TheoryK. Chandrasekaran and R. Karp. Finding a most biased coin with fewest flips. arXiv:1202.3639, 2012.
Biased-coin designs are used in clinical trials to allocate treatments with some randomness while maintaining approximately equal allocation. More recent rules are compared with Efron's [Biometrika 58 (1971) 403-417] biased-coin rule and extended to allow balance over covariates. The main properties...
We give new algorithms for simulating a flip of an unbiased coin by flipping a coin of unknown bias. We are interested in efficient algorithms, where the expected number of flips is our measure of efficiency. Other authors have represented algorithms as lattices, but by representing them instead...
Procedures are exhibited and analyzed for converting a sequence of i.i.d. Bernoulli variables with unknown mean p into a Bernoulli variable with mean 1/2. The efficiency of several procedures is studied.
Clearly, the biased coin has the same probability of gettingTAILSand thenHEADSas the probability of gettingHEADSand thenTAILS. So if we exclude the events of twoHEADSand twoTAILSby repeating the procedure, we are left with the only two remaining outcomes having equivalent probability. That’s th...
We also need a fair coin simulator. For this simulation, let’s just use Python’s built-in pseudo-random number generator: deffairCoin():returnrandom.choice([0,1]) Let us toss our biased coin 10000 times and take the sum. We expect the sum to be around 3333. Indeed, when I tried...