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11: Bernoulli Trials

  • Page ID
    10232
  • The Bernoulli trials process is one of the simplest, yet most important, of all random processes. It is an essential topic in any course in probability or mathematical statistics. The process consists of independent trials with two outcomes and with constant probabilities from trial to trial. Thus it is the mathematical abstraction of coin tossing. The process leads to several important probability distributions: the binomial, geometric, and negative binomial.

    • 11.1: Introduction to Bernoulli Trials
      The Bernoulli trials process, named after Jacob Bernoulli, is one of the simplest yet most important random processes in probability. Essentially, the process is the mathematical abstraction of coin tossing, but because of its wide applicability, it is usually stated in terms of a sequence of generic trials.
    • 11.2: The Binomial Distribution
      In this section we will study the random variable that gives the number of successes in the first n trials and the random variable that gives the proportion of successes in the first n trials. The underlying distribution, the binomial distribution, is one of the most important in probability theory, and so deserves to be studied in considerable detail. As you will see, some of the results in this section have two or more proofs.
    • 11.3: The Geometric Distribution
    • 11.4: The Negative Binomial Distribution
    • 11.5: The Multinomial Distribution
    • 11.6: The Simple Random Walk
      The simple random walk process is a minor modification of the Bernoulli trials process. Nonetheless, the process has a number of very interesting properties, and so deserves a section of its own. In some respects, it's a discrete time analogue of the Brownian motion process.
    • 11.7: The Beta-Bernoulli Process
      An interesting thing to do in almost any parametric probability model is to randomize one or more of the parameters. Done in a clever way, this often leads to interesting new models and unexpected connections between models. In this section we will randomize the success parameter in the Bernoulli trials process. This leads to interesting and surprising connections with Pólya's urn process.

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