Importance Sampling
أخذ العيّنات المُرجَّحة
تقنية إحصائية تتيح تقدير خصائص توزيع احتمالي باستخدام عيّنات مأخوذة من توزيع مختلف، وذلك بترجيح كل عيّنة بنسبة احتمالها في التوزيعين.
A statistical technique that allows estimating properties of one probability distribution using samples from a different distribution, by weighting each sample by its probability ratio under the two distributions.
Also translated asالاعتيان بالأهمية، الاعتيان الوزني
First appears in this corpus in: The Monte Carlo Method (1949)
Appears in these papers
- Conservative Q-Learning for Offline Reinforcement Learning2020in the sky ✦
- IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures2018in the sky ✦
- IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures2018in the sky ✦
- Equation of State Calculations by Fast Computing Machines1953in the sky ✦
- The Monte Carlo Method1949in the sky ✦
- Prioritized Experience Replay2015in the sky ✦
- Prioritized Experience Replay2015in the sky ✦
- Rainbow: Combining Improvements in Deep Reinforcement Learning2018in the sky ✦
- Trust Region Policy Optimization2015in the sky ✦
- Trust Region Policy Optimization2015in the sky ✦