In article <[EMAIL PROTECTED]>, Herman Rubin <[EMAIL PROTECTED]> wrote:
Major error, with correction! >In article <[EMAIL PROTECTED]>, >nicolexz <[EMAIL PROTECTED]> wrote: >>I need to sample a random variable from truncated distribution >>everytime in MCMC. Suppose, the upper (a) and lower (b) bounds are >>far from the location parameter (c) and the scale parameter is >>relatively small, i.e,. b<=a<=c, and c is far greater than a and b. >>The chance to sample using slice sampling is trivival. It's almost >>impossible to sample it from such a truncated distribution. However, >>the problem is quite often in some cases. What should I do in dealing >>with this scenario? <Consider sampling from the truncated normal (0,1) distribution, <truncated at u and v. A non-optimal method is to use the same <truncation points on a distribution whose density is f(x)/3, <where < f(x) = 1, -1<=x<=1 * f(x) = exp(2(x+1)), x<=-1 * f(x) = exp(-2(x-1)), 1<=x <and accept if a test exponential variable T satisfies <T - ln(f(x)) - (x^2)/2 > 0, saving T if it does, and <rejecting otherwise. Truncated procedures can be based <on this. However, if the range is outside the interval <(-2,2), this is not too good. If the range is u to v, <where 2 < u < v, a crude method is to let x - u have <a truncated exponential distribution with density <u*exp(-uz), and accept if T - (z^2)/2 > 0. -- This address is for information only. I do not claim that these views are those of the Statistics Department or of Purdue University. Herman Rubin, Department of Statistics, Purdue University [EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558 . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
