en-es
en-fr
en-sl
en
0.25
0.5
0.75
1.25
1.5
1.75
2
Building apriori knowledge into conclusions drawn from simulations
Published on Sep 01, 20161659 Views
Related categories
Chapter list
Building apriori knowledge into conclusions drawn from simulations00:00
Finite forms of de Finetti's theorem - 101:48
Finite forms of de Finetti's theorem - 202:06
Thinking about Monte Carlo Data03:40
Example - 105:15
Idea06:34
Do the Numbers Mean Anything?10:13
First Thoughts11:43
ALAS17:24
So!19:26
Idea 322:56
Knuth's Problem25:41
Many Similiar Problems27:55
Example - 231:00
Solutions33:14
Points36:30
Back to Big Picture38:38
Shifting our views on Monte Carlo43:26
Larry’s constant44:22
Example 11.1045:05
Example 11.10: “the” argument45:35
“Bayesians are slaves to the likelihood function”46:15
a broader picture47:10
reverse logistic regression48:53
regression estimator49:06
statistical framework?50:26
partition function and maximum likelihood - 150:43
partition function and maximum likelihood - 251:12
Xiao-Li’s MLE - 151:15
Xiao-Li’s MLE - 251:35
Xiao-Li’s MLE - 352:08
Xiao-Li’s MLE - 452:13
Xiao-Li’s MLE - 552:31
reverse logistic regression52:58
Persi’s probabilistic numerics - 153:28
Persi’s probabilistic numerics - 255:05
My uncertainties55:29
a shift on Monte Carlo approximations56:33
Discussion58:53
Simple Methods for Output Analysis01:00:07
Bayesian Output Analysis01:01:46
Self-Avoiding Random Walk - 101:02:44
Self-Avoiding Random Walk - 201:03:31
Self-Avoiding Random Walk - 301:05:24
Conclusions01:06:26