Geology 6600/7600 Signal Analysis 04 Sep 2014 © A.R. Lowry 2015 Last time: Signal Analysis is a set of tools used to extract information from sequences.

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Presentation transcript:

Geology 6600/7600 Signal Analysis 04 Sep 2014 © A.R. Lowry 2015 Last time: Signal Analysis is a set of tools used to extract information from sequences of random variables… “Review” of multivariate statistics: Joint multivariate distribution function: Joint probability density function: Marginal densities: Independent RV’s:

Geology 6600/7600 Signal Analysis Last time (Cont’d): “Review” of multivariate statistics: Mean of a function g of random variables: Vector mean of a sequence of independent random variables: Covariance of two random variables is: (= 0 if independent).

The correlation R ij differs from the covariance in that the mean is not subtracted within the expectation operator: (although R and C will be the same if the variables are zero-mean). Thus if variables are independent, R ij =  i  j. In matrix form we can write: And for uncorrelated random variables,

Recall from last time that the multivariate normal distribution function is given by: At the time, C and  came outta nowhere… Now, we have an inkling of what they are! Exercise: show that for the case in which all random variables are uncorrelated,

Application: Consider a sum of independent, identically-distributed random variables (example: independent measurements of an unchanging property): The expected value of the sum is: Recall the variance so: Hence, the standard deviation on multiple measurements of the expected value of a property decreases as 1/ !

This would imply that, if x ’s are Gaussian*, so is z … True? “Recall” that the Fourier transform is defined as: We can express the Fourier transform of z as a characteristic function: So if x ’s are independent, From this one can show (advanced exercise?) that if each x i is N( ,  ), then z is N( ,  / ) the Fourier transform of a Gaussian function is also a Gaussian function ~ ~ ~~ ~~ * “Gaussian” and “normal” distribution are used interchangeably in this class.

The Central Limit Theorem states that as N , z tends to a normal distribution regardless of the distributions of the individual x i ’s… I.e., the random variables do not have to be identically distributed in order for the sum to be Gaussian. ~ ~