probability

问题 I've got a dataframe which has probabilities for different events over a large number of sequential periods, and I want to transform this df to show the probability of something happening at least once over n periods. eg, I've got this, which would be n = 1: event | period | probability A | period 1 | 0.6 A | period 2 | 0.7 A | period 3 | 0.8 A | period 4 | 0.85 A | period 5 | 0.9 And I want to figure out the probability of A occurring at least once across two periods (n = 2), which would be:

问题 Consider I have a million of observations following Gamma distribution with parameters (3,5). I am able to find the quantiles using summary() but I am trying to find how many observations are between each red lines which were divided into 10 pieces? a = rgamma(1e6, shape = 3, rate = 5) summary(a) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0053 0.3455 0.5351 0.6002 0.7845 4.4458 回答1: We may use cut with table : table(cut(a, quantile(a, 0:10 / 10))) # (0.00202,0.22] (0.22,0.307] (0.307,0.382] (0

问题 Consider I have a million of observations following Gamma distribution with parameters (3,5). I am able to find the quantiles using summary() but I am trying to find how many observations are between each red lines which were divided into 10 pieces? a = rgamma(1e6, shape = 3, rate = 5) summary(a) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0053 0.3455 0.5351 0.6002 0.7845 4.4458 回答1: We may use cut with table : table(cut(a, quantile(a, 0:10 / 10))) # (0.00202,0.22] (0.22,0.307] (0.307,0.382] (0

问题 I am currently working on a java application where I need to calculate the probabilities of rolling each sum for a variety of dice. The dice types I am supporting are d4 (4 sided dice), d6 (6 sided dice), d8 (8 sided dice), d10, d12, and d20. The user will be able to input the number of each type of dice they want to use in the calculation. For example, a user may enter 6 d6 and 4 d4. With this given information (the number of dice of each type), I am looking to calculate the probability that

问题 I am looking for some precise math on the likelihood of collisions for MD5, SHA1, and SHA256 based on the birthday paradox. I am looking for something like a graph that says "If you have 10^8 keys, this is the probability. If you have 10^13 keys, this is the probability and so on" I have looked at tons of articles but I am having a tough time finding something that gives me this data. (Ideal option for me would be a formula or code that calculates this for any provided hash size) 回答1: Let's

问题 I am looking for some precise math on the likelihood of collisions for MD5, SHA1, and SHA256 based on the birthday paradox. I am looking for something like a graph that says "If you have 10^8 keys, this is the probability. If you have 10^13 keys, this is the probability and so on" I have looked at tons of articles but I am having a tough time finding something that gives me this data. (Ideal option for me would be a formula or code that calculates this for any provided hash size) 回答1: Let's

问题 I am new to Python and Machine learning. I have searched internet regarding my question and tried the solution people have suggested, but still not get it. Would really appreciate it if anyone can help me out. I am working on my first XGboost model. I have tuned the parameters by using xgb.XGBClassifier, and then would like to enforce monotonicity on model variables. Seemingly I have to use xgb.train() to enforce monotonicity as shown in my code below. xgb.train() can do predict(), but NOT

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 7 days ago . Improve this question Is there a fast way in R to find fisher's information and cramer rao lower bound? I have this data - > dput(my_vec) c(7.16523478153752, 5.66659652818595, 4.47575534893755, 4.84970857977856, 15.2276296414708, -0.573093658844655, 4.97980673868322, 2.73969325233614, 5

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 7 days ago . Improve this question Is there a fast way in R to find fisher's information and cramer rao lower bound? I have this data - > dput(my_vec) c(7.16523478153752, 5.66659652818595, 4.47575534893755, 4.84970857977856, 15.2276296414708, -0.573093658844655, 4.97980673868322, 2.73969325233614, 5

问题 Git allows to retrieve the hash of the commit with commands like: git rev-parse HEAD which gives 33b316c or git rev-parse --short HEAD which gives 33b316cbeeab3d69e79b9fb659414af4e7829a32 I know that long hashes in practice will never collide. In practice, the short hashes are used much more often. I'd like to know what's the probability for the short ones to collide? Does git take any measures to overcome possible collisions (when for example using git checkout )? 回答1: I give a formula in my