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Random Process Engineering
Fichas del conjunto (34)
- 1Stochastic SystemSystems whos state is non-deterministic (random) so that the subsequent state of the system is found probabilistically.
- 2Stochastic MatrixMatrix who's values are non-negative and sum to 1
- 3Probability DistributionA math function which describes possible values of a random variable and their associated probabilities.
- 4Random VariableCan take on a set of possible values, each with an associated probability.
Yet to be or previously done experiment.
Objective random process (dice roll)
or Subjective randomness from incomplete knowledge of a quantity.
Can be discrete, taking on a specific finite or countable list of values, endoweded with a probability mass function, characteristic of a prob dist.
or Continuous, taking any numerical value in an intervall or collection of intervals, via a PDF that is characteristic of a Prob Dist
Or a mix of both - 5Random VariatesResults of choosing values according to the variables PDF
- 6Mean Value
- 7Variance
- 8Standard diviationsqrt(s^2) = s
- 9Normalization# of observations / total # of samples
- 10Coefficients of VariationStandard Deviation / mean
s/mean - 11SubsetC = a group within the set, can exist in more than one subset but only once in a set or subset
- 12UnionU = elements in either A or B or both but no element repeats
- 13IntersectionSame event twice
- 14Set Differencewhat values dont repeat in either A or B
- 15ComplementIf A is a subset of U, U\A is a complement of A in U.
- 16De Morgan's Law
- 17Relative Frequency
- 18sample spaceall possible outcomes. Zeta is an element of it.
- 19Joint Probability mass function
- 20Marginal Freq Function
- 21—probability for jointly distributed variables
- 22Baye's rule
- 23Permutation DefinitionAn ordered arrangement of objects
- 24Sampling with replacement, ordered samplesn*n*n*..=n^r possibilities
- 25Sampling without replacement, ordered samplesn*(n-1)...(n-r+1)=n!
- 26taking r objects from n objects, how many different samples are possiblen!/(n-r)!r!
Binomial Coefficient - 27Any particular sequence of successes occurs with Prob?k= total number of success
- 28Binomial Distribution
- 29PdF
- 30CdF
- 31Expected value of Continuous Distribution
- 32muexpected value
- 33Variance of Continuous Distribution
- 34Central Limit TheoremIf we sample from any distribution, normal or not, the sample mean will be approximately normally distributed, provided the sample size, n, is large, n>30.
Here mu_xbar = mu
sigma_xbar = sigma/sqrt(n)
Z = (xbar-mu)/(sigma/sqrt(n))
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- Descripción
- Fichas sobre Random Process Engineering, creado por Igor Tchouiko el 22/01/2019.
- Creado por
- Igor Tchouiko
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