likelihood

Examples of likelihood in the following topics:

• Estimating the Target Parameter: Point Estimation

  • A popular method of estimating the parameters of a statistical model is maximum-likelihood estimation (MLE).
  • When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters.
  • The method of maximum likelihood corresponds to many well-known estimation methods in statistics.
  • In general, for a fixed set of data and underlying statistical model, the method of maximum likelihood selects the set of values of the model parameters that maximizes the likelihood function.
  • However, in some complicated problems, maximum-likelihood estimators are unsuitable or do not exist.

• Introduction

  • Politicians study polls to guess their likelihood of winning an election.
  • You may have visited a casino where people play games chosen because of the belief that the likelihood of winning is good.

• Probability

  • Probability density function describes the relative likelihood, or probability, that a given variable will take on a value.
  • In probability theory, a probability density function (pdf), or density of a continuous random variable, is a function that describes the relative likelihood for this random variable to take on a given value.

• Probability

  • It deals with the chance (the likelihood) of an event occurring.
  • To predict the likelihood of an earthquake, of rain, or whether you will get an A in this course, we use probabilities.

• Information and Risk Trade-Off

  • Risk is the product of the likelihood of an occurrence times its impact (Risk = Likelihood x Impact).

• Variation in Samples

  • Even if Doreen and Jung used the same sampling method, in all likelihood their samples would be different.
  • But still, their samples would be, in all likelihood, different from each other.

• The Goals of an Informative Speech

  • And similar to a soccer match, hitting these goals increases the likelihood of a successful speech .
  • To make sure that the information contained in a speech is remembered by the audience, the deliverer of an informative speech should combine organization, repetition and focused visualizations to increase the effectiveness of the speech and the likelihood that the audience will leave informed.

• Make It Memorable

  • Remember, the goal of making an informative speech memorable is to increase the likelihood that your audience will walk away informed.
  • Making your informative speech memorable increases the likelihood that your audience will retain its information.

• Interpreting a Confidence Interval

  • Likelihood theory - The theory here is for estimates constructed using the maximum likelihood principle.
  • Estimating equations - The estimation approach here can be considered as both a generalization of the method of moments and a generalization of the maximum likelihood approach.
  • There are corresponding generalizations of the results of maximum likelihood theory that allow confidence intervals to be constructed based on estimates derived from estimating equations.

• Reinforcement and Punishment

  • Reinforcement and punishment are principles of operant conditioning that increase or decrease the likelihood of a behavior.
  • Reinforcement means you are increasing a behavior: it is any consequence or outcome that increases the likelihood of a particular behavioral response (and that therefore reinforces the behavior).
  • Punishment means you are decreasing a behavior: it is any consequence or outcome that decreases the likelihood of a behavioral response.