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In information theory and statistics, negentropy is used as a measure of distance to normality. [4] [5] [6] Out of all distributions with a given mean and variance, the normal or Gaussian distribution is the one with the highest entropy.
Biological processes are regulated by many means; examples include the control of gene expression, protein modification or interaction with a protein or substrate molecule. Homeostasis: regulation of the internal environment to maintain a constant state; for example, sweating to reduce temperature
In stochastic analysis a random process is a predictable process if it is possible to know the next state from the present time. The branch of mathematics known as Chaos Theory focuses on the behavior of systems that are highly sensitive to initial conditions. It suggests that a small change in an initial condition can completely alter the ...
In stochastic analysis, a part of the mathematical theory of probability, a predictable process is a stochastic process whose value is knowable at a prior time. The predictable processes form the smallest class that is closed under taking limits of sequences and contains all adapted left-continuous processes. [clarification needed]
The concept of allostasis, maintaining stability through change, is a fundamental process through which organisms actively adjust to both predictable and unpredictable events... Allostatic load refers to the cumulative cost to the body of allostasis, with allostatic overload... being a state in which serious pathophysiology can occur...
Then there exist a martingale M = (M n) n∈I and an integrable predictable process A = (A n) n∈I starting with A 0 = 0 such that X n = M n + A n for every n ∈ I. Here predictable means that A n is -measurable for every n ∈ I \ {0}. This decomposition is almost surely unique. [2] [3] [4]
Instead of dealing with only one possible reality of how the process might evolve over time (as is the case, for example, for solutions of an ordinary differential equation), in a stochastic or random process there is some indeterminacy in its future evolution described by probability distributions. This means that even if the initial condition ...
Common and special causes are the two distinct origins of variation in a process, as defined in the statistical thinking and methods of Walter A. Shewhart and W. Edwards Deming. Briefly, "common causes", also called natural patterns , are the usual, historical, quantifiable variation in a system, while "special causes" are unusual, not ...