montecarlo

问题 Here is the algorithm of what I want to do with R : Simulate 10 time series data set from ARIMA model through arima.sim() function Split the series into sub-series of possible 2s , 3s , 4s , 5s , 6s , 7s , 8s , and 9s . For each size take a resample the blocks with replacement, for new series and obtain the best ARIMA model from the subseries from each block size through auto.arima() function. Obtain for each subseries of each block sizes RMSE . The below R function get that done. ## Load

问题 Here is the algorithm of what I want to do with R : Simulate 10 time series data set from ARIMA model through arima.sim() function Split the series into sub-series of possible 2s , 3s , 4s , 5s , 6s , 7s , 8s , and 9s . For each size take a resample the blocks with replacement, for new series and obtain the best ARIMA model from the subseries from each block size through auto.arima() function. Obtain for each subseries of each block sizes RMSE . The below R function get that done. ## Load

来源： https://stackoverflow.com/questions/62249186/how-to-use-prange-in-cython

来源： https://stackoverflow.com/questions/63967198/calculate-expected-value-of-variance-using-monte-carlo-simulation

问题 I am using trying to use bridge sampling in R studio to simulate paths for the variance gamma process. My code is: sigma = 0.5054 theta = 0.2464 nu = 0.1184 mu=1 N=2^(k) k=5 V_<-rep(NA,252) V_[0]<-0 G_[N]<-rgamma(1, shape=N*1/nu, scale=nu) G_<-0 V<-rnorm(theta*G[N],sigma^2*G[N]) for(l in 1:k){ n<-2^(k-l) for(j in 1:2^i-1){ i<-(2*j-1)*n d1<-(n)*mu^2/nu d2<-(n)*mu^2/nu Y<-rbeta(1,d1,d2) G_[i]<-G_[i-1]+(G[i+n]-G[i-n])*Y G[i] print(G_[i]) Z<-rnorm(0,(G_[i+n]-G_[i])*sigma^2*Y) V_[i]<-Y*V_[i+n]+(1

问题 I had a small but potentially stupid question about Monte Carlo Tree Search. I understand most of it but have been looking at some implementations and noticed that after the MCTS is run for a given state and a best move returned, the tree is thrown away. So for the next move, we have to run MCTS from scratch on this new state to get the next best position. I was just wondering why we don't retain some of the information from the old tree. It seems like there is valuable information about the

问题 I had a small but potentially stupid question about Monte Carlo Tree Search. I understand most of it but have been looking at some implementations and noticed that after the MCTS is run for a given state and a best move returned, the tree is thrown away. So for the next move, we have to run MCTS from scratch on this new state to get the next best position. I was just wondering why we don't retain some of the information from the old tree. It seems like there is valuable information about the

问题 I am trying to solve the following exercise: Let Z_n be maximum of n standard normal observations. Estimate what n should be so that P(Z_n>4)=0.25 I have tried following code and I know the answer is about n=9000 because it returns aproximately 0.25. I should change my code so that n is the output and not the input. n=9000 x1 <- sapply(1:n, function(i){max(rnorm(n=n,0,1))}) length(x1[x1>4])/length(x1) How can I do that? Thanks for helping! 回答1: Well, you could select appropriate range and

问题 I am trying to solve the following exercise: Let Z_n be maximum of n standard normal observations. Estimate what n should be so that P(Z_n>4)=0.25 I have tried following code and I know the answer is about n=9000 because it returns aproximately 0.25. I should change my code so that n is the output and not the input. n=9000 x1 <- sapply(1:n, function(i){max(rnorm(n=n,0,1))}) length(x1[x1>4])/length(x1) How can I do that? Thanks for helping! 回答1: Well, you could select appropriate range and

问题 I have written the program below and keep getting the error message that my variables are not defined. Can somebody plese see where the error is and how I should adapt the code? Really nothing seems to work. program define myreg, rclass drop all set obs 200 gen x= 2*uniform() gen z = rnormal(0,1) gen e = (invnorm(uniform()))^2 e=e-r(mean) replace e=e-r(mean) more gen y = 1 + 1*x +1*z + 1*e reg y x z e=e-r(mean) replace e=e-r(mean) more gen y = 1 + 1*x +1*z + 1*e reg y x z more return scalar