201,278 research outputs found
An approximation scheme for quasi-stationary distributions of killed diffusions
In this paper we study the asymptotic behavior of the normalized weighted
empirical occupation measures of a diffusion process on a compact manifold
which is killed at a smooth rate and then regenerated at a random location,
distributed according to the weighted empirical occupation measure. We show
that the weighted occupation measures almost surely comprise an asymptotic
pseudo-trajectory for a certain deterministic measure-valued semiflow, after
suitably rescaling the time, and that with probability one they converge to the
quasi-stationary distribution of the killed diffusion. These results provide
theoretical justification for a scalable quasi-stationary Monte Carlo method
for sampling from Bayesian posterior distributions.Comment: v2: revised version, 29 pages, 1 figur
Improved Weighted Random Forest for Classification Problems
Several studies have shown that combining machine learning models in an
appropriate way will introduce improvements in the individual predictions made
by the base models. The key to make well-performing ensemble model is in the
diversity of the base models. Of the most common solutions for introducing
diversity into the decision trees are bagging and random forest. Bagging
enhances the diversity by sampling with replacement and generating many
training data sets, while random forest adds selecting a random number of
features as well. This has made the random forest a winning candidate for many
machine learning applications. However, assuming equal weights for all base
decision trees does not seem reasonable as the randomization of sampling and
input feature selection may lead to different levels of decision-making
abilities across base decision trees. Therefore, we propose several algorithms
that intend to modify the weighting strategy of regular random forest and
consequently make better predictions. The designed weighting frameworks include
optimal weighted random forest based on ac-curacy, optimal weighted random
forest based on the area under the curve (AUC), performance-based weighted
random forest, and several stacking-based weighted random forest models. The
numerical results show that the proposed models are able to introduce
significant improvements compared to regular random forest
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