A random, or stochastic, process results in outcomes that cannot be predicted precisely. The outc... more A random, or stochastic, process results in outcomes that cannot be predicted precisely. The outcome of a random process, a random variable, is described by its probability of occurrence. Probabilities range from 0, no chance, to 1, a certainty. There are different interpretations of probability, notably the f requentist and Bayesian views. Frequentists consider a set of repeated experiments or trials with probability expressed by the frequency of occurrence, i.e., if A occurs n A times out of n experiments, then the probability of A is P r(A) = n A /n. (1) In the frequentist view, Pr(A) is assumed to approach the true probability of A as n → ∞. In the Bayesian approach, existing knowledge is used to assign probability beforehand, the prior probability, which is updated to a posterior probability based on the data, or evidence, and the application of Bayes' theorem. Here we will examine frequentist methodologies (e.g., confidence intervals, hypothesis testing) commonly used in the analysis of oceanographic data. There are two related functions that assign probabilities to a random variable. The cumulative distribution function (CDF) specifies the probability that the random variable, X, is less than or equal to a specific value x,
A random, or stochastic, process results in outcomes that cannot be predicted precisely. The outc... more A random, or stochastic, process results in outcomes that cannot be predicted precisely. The outcome of a random process, a random variable, is described by its probability of occurrence. Probabilities range from 0, no chance, to 1, a certainty. There are different interpretations of probability, notably the f requentist and Bayesian views. Frequentists consider a set of repeated experiments or trials with probability expressed by the frequency of occurrence, i.e., if A occurs n A times out of n experiments, then the probability of A is P r(A) = n A /n. (1) In the frequentist view, Pr(A) is assumed to approach the true probability of A as n → ∞. In the Bayesian approach, existing knowledge is used to assign probability beforehand, the prior probability, which is updated to a posterior probability based on the data, or evidence, and the application of Bayes' theorem. Here we will examine frequentist methodologies (e.g., confidence intervals, hypothesis testing) commonly used in the analysis of oceanographic data. There are two related functions that assign probabilities to a random variable. The cumulative distribution function (CDF) specifies the probability that the random variable, X, is less than or equal to a specific value x,
Uploads
Papers by Vincent Kiprop