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Maximum Likelihood vs. Sequential Normalized Maximum Likelihood in On-line Density Estimation
Published on Aug 02, 20113775 Views
The paper considers sequential prediction of individual sequences with log loss (online density estimation) using an exponential family of distributions. We first analyze the regret of the maximum
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Chapter list
Maximum Likelihood vs. Sequential Normalized Maximum Likelihood in On-line Density Estimation00:00
Outline00:16
Sequential Prediction02:38
Sequential Prediction: Example04:43
Avoiding Infinite Regret05:59
Maximum Likelihood Strategy08:19
Regret Bound for ML Strategy10:18
Improving ML Strategy11:46
SNML: Examples (without projection)12:55
Regret Bound for SNML Strategy14:06
Example: ML vs. SNML - 114:41
Example: ML vs. SNML - 215:46
Example: ML vs. SNML - 315:47
Example: ML vs. SNML - 415:48
Example: ML vs. SNML - 515:49
Example: ML vs. SNML - 615:55
SNML and Bayes - 116:00
SNML and Bayes - 216:43
Conclusions17:58