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A sigmoid function is any mathematical function whose graph has a characteristic S-shaped or sigmoid curve. A common example of a sigmoid function is the logistic function , which is defined by the formula: [ 1 ]
The standard logistic function is the logistic function with parameters =, =, =, which yields = + = + = / / + /.In practice, due to the nature of the exponential function, it is often sufficient to compute the standard logistic function for over a small range of real numbers, such as a range contained in [−6, +6], as it quickly converges very close to its saturation values of 0 and 1.
Excel graph of the difference between two evaluations of the smallest root of a quadratic: direct evaluation using the quadratic formula (accurate at smaller b) and an approximation for widely spaced roots (accurate for larger b). The difference reaches a minimum at the large dots, and round-off causes squiggles in the curves beyond this minimum.
The generalized logistic function or curve is an extension of the logistic or sigmoid functions. Originally developed for growth modelling, it allows for more flexible S-shaped curves. The function is sometimes named Richards's curve after F. J. Richards, who proposed the general form for the family of models in 1959.
The function also adheres to the sigmoid function, which is the most widely accepted convention of generally detailing a population's growth. Moreover, the function makes use of initial growth rate, which is commonly seen in populations of bacterial and cancer cells, which undergo the log phase and grow rapidly in numbers. Despite its ...
The curve shows the probability of passing an exam versus hours studying. The logistic function is of the form: = + / where μ is a location parameter (the midpoint of the curve, where () = /) and s is a scale parameter. This expression may be rewritten as:
Compensated demand curve; Duck curve; Engel curve; Hubbert curve; Indifference curve; J curve; Kuznets curve; Laffer curve; Lorenz curve; Phillips curve; Supply curve. Aggregate supply curve; Backward bending supply curve of labor
The Johnson's S U-distribution is a four-parameter family of probability distributions first investigated by N. L. Johnson in 1949. [ 1 ] [ 2 ] Johnson proposed it as a transformation of the normal distribution : [ 1 ]