Estimating the parameters of gaussians to fit a lot of samples can be do with...

dream13rxs

dream13rxs

Answered

2022-07-08

Estimating the parameters of gaussians to fit a lot of samples can be do with Exceptation Maximization, for instance if we want to fit two gaussian on points, to have the clusters a and b. (1)
b i = P ( b | x i ) = P ( x i | b ) P ( b ) P ( x i | b ) P ( b ) + P ( x i | b ) P ( a )
Here P ( b ) is the prior that depicts the overall importance of the b cluster.
This prior is then updated for the next step, according on how many the points belongs to the b cluster:
P ( b ) = 1 n i b i
However, what is the value of the prior P ( b ) on the first iteration of the algorithm?

Answer & Explanation

Sophia Mcdowell

Sophia Mcdowell

Expert

2022-07-09Added 14 answers

Some simple suggestions: you could initialize with k-means and/or run multiple tries with different random initialization. Note that it is possible for EM algorithms to get stuck in local minima, so multiple initializations can be useful.

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