"Monte Carlo simulations (MCSs) provide important information about statistical phenomena that would be impossible to assess otherwise. This article introduces MCS methods and their applications to research and statistical pedagogy using a novel software package for the R Project for Statistical Computing constructed to lessen the often steep learning curve when organizing simulation code. A primary goal of this article is to demonstrate how well-suited MCS designs are to classroom demonstrations, and how they provide a hands-on method for students to become acquainted with complex statistical concepts. In this article, essential programming aspects for writing MCS code in R are overviewed, multiple applied examples with relevant code are provided, and the benefits of using a generate–analyze–summarize coding structure over the typical "for-loop" strategy are discussed."
Imagine your task as Amy or Andy analyst is to tell finance how much to budget for sales commissions for next year. One approach might be to assume everyone makes 100% of their target and earns the 4% commission rate. Imagine you present this to finance, and they say, "We never have everyone get the same commission rate. We need a more accurate model." Now, you have a little bit more information and go back to finance.
Rafael Nadal once again showed why he is the greatest clay court player in the history of the game with a dominant run to the title at the Monte Carlo Masters, his 11th in the Principality of Monaco. The Spaniard defeated Kei Nishikori in straight sets in the final on Sunday and won the title without dropping a set throughout the tournament. He has now won 36 straight sets since the start of the 2017 French Open, where he captured his 10th title last year. Nadal's win in Monte Carlo was more significant as it was his first ATP event of 2018 having struggled with a hip injury since January 2018. He withdrew from the Australian Open with the injury which subsequently saw him withdraw from the Mexican Open, the Indian Wells Masters and the Miami Open.
World No. 1 Rafael Nadal will look to capture his 11th Monte Carlo Masters title on Sunday when he faces Kei Nishikori in the final. Betting site Betfair Exchange lists Nadal as a 1/14 favorite, while Nishikori is a 59/5 longshot. Nadal, who owns just a 100-point lead on No. 2 Roger Federer, defeated No. 5 Grigor Dimitrov 6-4 6-1 on Saturday, after No. 36 Nishikori held off No. 4 Alexander Zverev, 3-6 6-3 6-4. The Spaniard needs a victory Sunday to remain No. 1. Federer has been inactive since an upset loss to Thanasi Kokkinakis at the Miami Open on March 24. Prior to the start of the Monte Carlo Masters, Nadal was the overwhelming favorite with 8/11 odds and has done little to shed doubt of his frontrunner status.
Monte Carlo tree search has brought significant improvements to the level of computer players in games such as Go, but so far it has not been used very extensively in games of strongly imperfect information with a dynamic board and an emphasis on risk management and decision making under uncertainty. In this paper we explore its application to the game of Kriegspiel (invisible chess), providing three Monte Carlo methods of increasing strength for playing the game with little specific knowledge. We compare these Monte Carlo agents to the strongest known minimax-based Kriegspiel player, obtaining significantly better results with a considerably simpler logic and less domain-specific knowledge.