[原文链接:http://engineering.richrelevance.com/recommendations-thompson-sampling/.] [本文链接:http://www.cnblogs.com/breezedeus/p/3775339.html,转载请注明出处] Recommendations with Thompson Sampling 06/05/2014 • Topics: Bayesian, Big data, Data Science by Sergey Fel…

1.IIR滤波器构造           之前在介绍FIR滤波器的时候,我们提到过,IIR滤波器的单位冲击响应是无限的!用差分方程来表达一个滤波器,应该是下式这个样子的.                    这个式子是N次差分方程的表达式.我们明显可以看出,计算输出y(n)的时候,需要以前的输出值与输入值.换言之,这个可能表达式还有反馈环节.当为0的时候,这个滤波器由于没有反馈,其单位冲击响应是有限的,是FIR滤波器.当不为0是时候,是IIR滤波器. 2.直接I型IIR滤波器 就如同之前所说一…

[原文链接:http://engineering.richrelevance.com/bandits-recommendation-systems/.] [本文链接:http://www.cnblogs.com/breezedeus/p/3775316.html,转载请注明出处] Bandits for Recommendation Systems 06/02/2014 • Topics: Bayesian, Big data, Data Science by Sergey Feldman Th…

https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644   How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…

适用于在线服务的A/B测试方法论 简介: 这篇文章旨在介绍适用于为在线服务进行A/B测试(A/B Test)的方法论.中文网络中目前还缺乏全面的入门级介绍. 我将首先讨论在线服务业进行A/B测试所考虑的决策问题,然后介绍两种不太常见的统计测试:序列概率比测试(Sequential Probability Ratio Test, SPRT)和基于多臂bandit(Multi-armed Bandit, MAB)算法的测试. I.不只是P-Value 经典统计学会用如下的方法进行A/B测试: (F1…

 > 目  录 <  k-armed bandit problem Incremental Implementation Tracking a Nonstationary Problem Initial Values (*) Upper-Confidence-Bound Action Selection(UCB) (*) Gradient Bandit Algorithms (*) Associative Search (Contextual Bandits)  > 笔  记 < …

[原文链接] 选择是一个技术活 著名鸡汤学家沃.滋基硕德曾说过:选择比努力重要. 我们会遇到很多选择的场景.上哪个大学,学什么专业,去哪家公司,中午吃什么,等等.这些事情,都让选择困难症的我们头很大.那么,有办法能够应对这些问题吗? 答案是:有!而且是科学的办法,而不是“走近科学”的办法.那就是bandit算法! bandit算法来源于人民群众喜闻乐见的赌博学,它要解决的问题是这样的[1]: 一个赌徒,要去摇laohu机,走进赌场一看,一排laohu机,外表一模一样,但是每个laohu机吐钱的概…

Prioritized Experience Replay JAN 26, 2016 Schaul, Quan, Antonoglou, Silver, 2016 This Blog from: http://pemami4911.github.io/paper-summaries/2016/01/26/prioritizing-experience-replay.html Summary Uniform sampling from replay memories is not an effic…

1.Delayed, sparse reward(feedback), Long-term planning Hierarchical Deep Reinforcement Learning, Sub-goal, SAMDP, optoins, Thompson sampling, Boltzman exploration, Improving Exploration 2.Partial observability, Imperfect-Information Memory, Nash equi…

转载自: Introduction to MPI - Part II (Youtube) Buffering  Suppose we have ) MPI_Send(sendbuf,...,,...) ) MPI_Recv(recvbuf,...,,...) These are blocking communications, which means they will not return until the arguments to the functions can be safely m…