As far as I understand, the linked “neat comparison” in Wikipedia is actually about (-1)^S being the only unbiased estimator of exp(-2*theta) in the case N=1. For N>=2, there is I_{X_1=0, X_2=0} and its Rao-Blackwellization (1 – 2/N)^S.

]]>Christian ]]>

I see that you reviewed the book The Slow Regard of Silent Things (Kingkiller) before. I have written a book that is similar to that. Would you be willing to let me provide you with a copy of the book in hopes that you would consider reviewing my book as well?

My name is Charles D. Shell, and the book I want to send is titled Blood Calls. You can find a link to it here. (https://www.amazon.com/Blood-Calls-History-Book-ebook/dp/B00COJPCHQ/ref=asap_bc?ie=UTF8)

I can provide it to you as whatever digital file you wish. It’s up to you.

Of course, I understand that you are under no obligation to review my book, and if you do review it, all I ask is that you leave an honest review. I am simply looking for the opportunity to have you consider it.

Thank you. I look forward to your response.

Sincerely,

Charles D. Shell

]]>In my work, in each iteration I would like to use a normal proposal density for a parameter x_1 where the proposal mean and covariance matrix depend on the current values of parameters x_2 and x_3 which depend on the previous value of x_1 and so on. While this works well in practice, I was wondering if such a scheme preserves ergodicity (it seems it does!) and if to treat the proposal density as independent from the previous value of x_1 when calculating the acceptance ratio. Would highly appreciate any thoughts on this. ]]>