> > l want to have 1000 configurations at the end of the process of gibbs > sampling rather than one configuration.
So just take 1000 samples, i.e. run `gibbs(...)` 1000 times, preferably with different input data points (`vis` parameter). Note that the idea of contrastive divergence is to reduce number of Gibbs sampling iterations per datapoint. Normally, only 1 pass is used (i.e. CD-1). > i have another question related to the topic. When we would like to draw a > histogram for our gibbs samplaing what should be the axis of abscissa and > Ordinate ? I think the question is what you are trying to achieve. Gibbs sampling is used to produce samples from a highly multivariate distribution, so visualizing it using histograms doesn't sound very reasonable. Can you give some context of your work and reference to a library/project you are using if it's available. On Friday, August 19, 2016 at 5:45:57 PM UTC+3, Ahmed Mazari wrote: > > i have another question related to the topic. When we would like to draw a > histogram for our gibbs samplaing what should be the axis of abscissa and > Ordinate ? > > On Friday, August 19, 2016 at 4:23:24 PM UTC+2, Ahmed Mazari wrote: >> >> Hello, >> >> l want to have 1000 configurations at the end of the process of gibbs >> sampling rather than one configuration. How can l do that properly >> >> Thank you >> > -- You received this message because you are subscribed to the Google Groups "julia-stats" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
