Description Usage Arguments Details References Examples
This function generates a Markov chain using a random walk Metropolis-Hastings algorithm.The user supplies target distribution, burn-in time, the length of the chain and the variance of proposal distribution. And a Markov chain after discarding burn-in samples is returned, which can be used for monitoring convengence.
1 | Metropolis(burn_in=1000,dist=dcauchy,sigma,N=10000,print_acc=F)
|
burn_in |
the total length of discarding |
dist |
the target distribution |
sigma |
the variance of the normal proposal distribution |
N |
the length of Markov chain |
print_acc |
if print acceptance rate or not |
Metropolis generates a Markov chain using a Metropolis-Hastings algorithm. The aim is to generate random numbers from specific distribution based on Normal proposal distribution
Statistic computing with R. Maria L. Rizzo
1 2 3 4 5 | ## Not run:
y=Metropolis(sigma=2,print_acc=T)
plot(density(Metropolis(sigma = 3,print_acc = T)))
## End(Not run)
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