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In mathematics and statistics, a quantitative variable may be continuous or discrete if it is typically obtained by measuring or counting, respectively. [1] If it can take on two particular real values such that it can also take on all real values between them (including values that are arbitrarily or infinitesimally close together), the variable is continuous in that interval. [2]
Rapid automatized naming (RAN) is a task that measures how quickly individuals can name aloud objects, pictures, colors, or symbols (letters or digits). Variations in rapid automatized naming time in children provide a strong predictor of their later ability to read, and is independent from other predictors such as phonological awareness, verbal IQ, and existing reading skills. [1]
The goal of motor skill is to optimize the ability to perform the skill at the rate of success, precision, and to reduce the energy consumption required for performance. Performance is an act of executing a motor skill or task. Continuous practice of a specific motor skill will result in a greatly improved performance, which leads to motor ...
A discrete signal or discrete-time signal is a time series consisting of a sequence of quantities. Unlike a continuous-time signal, a discrete-time signal is not a function of a continuous argument; however, it may have been obtained by sampling from a continuous-time signal.
The schema theory illustrates that motor learning consists of continuous processes that update the recall and recognition schemas with each movement that is made. [ 7 ] p. 33 Multiple paired forward and inverse models
Dichotomization is the special case of discretization in which the number of discrete classes is 2, which can approximate a continuous variable as a binary variable (creating a dichotomy for modeling purposes, as in binary classification). Discretization is also related to discrete mathematics, and is an important component of granular computing.
Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method, [2] which uses mutual information to recursively define the best bins, CAIM, CACC, Ameva, and many others [3] Many machine learning algorithms are known to produce better models by discretizing continuous attributes.
Like the discrete-time Markov decision processes, in continuous-time Markov decision processes the agent aims at finding the optimal policy which could maximize the expected cumulated reward. The only difference with the standard case stays in the fact that, due to the continuous nature of the time variable, the sum is replaced by an integral: