Search results
Results from the WOW.Com Content Network
t. e. In probability theory, a probability space or a probability triple is a mathematical construct that provides a formal model of a random process or "experiment". For example, one can define a probability space which models the throwing of a die. A probability space consists of three elements: [1][2] A sample space, Ω {\displaystyle \Omega }
e. In probability theory, the sample space (also called sample description space, [ 1 ]possibility space, [ 2 ] or outcome space[ 3 ]) of an experiment or random trial is the set of all possible outcomes or results of that experiment. [ 4 ] A sample space is usually denoted using set notation, and the possible ordered outcomes, or sample points ...
Probability theory. In probability and statistics, a Bernoulli process (named after Jacob Bernoulli) is a finite or infinite sequence of binary random variables, so it is a discrete-time stochastic process that takes only two values, canonically 0 and 1. The component Bernoulli variables Xi are identically distributed and independent.
Because the while loop checks the condition/expression before the block is executed, the control structure is often also known as a pre-test loop. Compare this with the do while loop, which tests the condition/expression after the loop has executed. For example, in the languages C, Java, C#, [2] Objective-C, and C++, (which use the same syntax ...
Probability is the branch of mathematics concerning events and numerical descriptions of how likely they are to occur. The probability of an event is a number between 0 and 1; the larger the probability, the more likely an event is to occur. [note 1] [1] [2] A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the ...
t. e. Information theory is the mathematical study of the quantification, storage, and communication of information. The field was established and put on a firm footing by Claude Shannon in the 1940s, [ 1 ] though early contributions were made in the 1920s through the works of Harry Nyquist and Ralph Hartley.
A stochastic process is defined as a collection of random variables defined on a common probability space (,,), where is a sample space, is a -algebra, and is a probability measure; and the random variables, indexed by some set , all take values in the same mathematical space , which must be measurable with respect to some -algebra .
Each oval shape represents a random variable that can adopt any of a number of values. The random variable x(t) is the hidden state at time t (with the model from the above diagram, x(t) ∈ { x 1, x 2, x 3}). The random variable y(t) is the observation at time t (with y(t) ∈ { y 1, y 2, y 3, y 4}).