{"id":17418,"date":"2022-08-26T06:21:50","date_gmt":"2022-08-26T06:21:50","guid":{"rendered":"https:\/\/www.booksofall.com\/ru\/?post_type=product&p=17418"},"modified":"2022-08-26T06:22:05","modified_gmt":"2022-08-26T06:22:05","slug":"introductory-probability-grinstead-and-snell","status":"publish","type":"product","link":"https:\/\/www.booksofall.com\/ru\/introductory-probability-grinstead-and-snell\/","title":{"rendered":"Introductory Probability (Grinstead and Snell)"},"content":{"rendered":"

This text is designed for an introductory probability course taken by sophomores, juniors, and seniors in mathematics<\/a>, the physical and social sciences, engineering, and computer science<\/a>. It presents a thorough treatment of probability ideas and techniques necessary for a firm understanding of the subject. The text can be used in a variety of course lengths, levels, and areas of emphasis.<\/p>\n

We consider a dice game that played an important role in the historical development of probability. The famous letters between Pascal and Fermat, which many believe started a serious study of probability, were instigated by a request for help from a French nobleman and gambler, Chevalier de M\u00e9r\u00e9<\/a>. It is said that de M\u00e9r\u00e9 had been betting that, in four rolls of a die, at least one six would turn up. He was winning consistently and, to get more people to play, he changed the game to bet that, in 24 rolls of two dice, a pair of sixes would turn up. It is claimed that de M\u00e9r\u00e9 lost with 24 and felt that 25 rolls were necessary to make the game favorable. It was that mathematics was wrong.<\/p>\n

We shall try to see if de M\u00e9r\u00e9 is correct by simulating his various bets. The program DeMere1 simulates a large number of experiments, seeing, in each one, if a six turns up in four rolls of a die. When we ran this program for 1000 plays, a six came up in the first four rolls 48.6 percent of the time. When we ran it for 10,000 plays this happened 51.98 percent of the time.<\/p>\n

We note that the result of the second run suggests that de M\u00e9r\u00e9 was correct in believing that his bet with one die was favorable; however, if we had based our conclusion on the first run, we would have decided that he was wrong. Accurate results by simulation require a large number of experiments.<\/p>\n

In the second example Peter was ahead most of the time. It is a remarkable fact, however, that, if play is continued long enough, Peter\u2019s winnings will continue to come back to 0, but there will be very long times between the times that this happens. These and related results will be discussed in Chapter 12.<\/p>\n

In all of our examples so far, we have simulated equiprobable outcomes. We illustrate next an example where the outcomes are not equiprobable.<\/p>\n

Historical Remarks<\/h3>\n

Anyone who plays the same chance game over and over is really carrying out a simulation, and in this sense the process of simulation has been going on for centuries. As we have remarked, many of the early problems of probability might well have been suggested by gamblers\u2019<\/a> experiences.<\/p>\n

It is natural for anyone trying to understand probability theory to try simple experiments by tossing coins, rolling dice, and so forth. The naturalist Buffon tossed a coin 4040 times, resulting in 2048 heads and 1992 tails. He also estimated the number by throwing needles on a ruled surface and recording how many times the needles crossed a line The English biologist W. F. R. Weldon recorded 26,306 throws of 12 dice, and the Swiss scientist Rudolf Wolf<\/a> recorded 100,000 throws of a single die without a computer. Such experiments are very time-consuming and may not accurately represent the chance phenomena being studied. For example, for the dice experiments of Weldon and Wolf, further analysis of the recorded data showed a suspected bias in the dice.<\/p>\n","protected":false},"excerpt":{"rendered":"