# Understanding an example of coin toss expectation maximization [duplicate]

I've been trying to get my head around Expectation maximization algorithms, and I thought I'd start simple. I found this 3-coin example here: http://cs.dartmouth.edu/~cs104/CS104_11.04.22.pdf I understand the calculation of all of the probabilities but I don't understand how the recalculation of lambda, p1 and p2 is actually done. (Page 18)

I understand how the maximization of a log-likelihood/likelihood function by differentiation works, but can't figure out the recalculation method here.

Can anyone explain why the recalculation of lambda, p1 and p2 take the form they do?

• Please make this question a little bit more self-contained. Jun 21, 2013 at 11:49
• See this question. It covers that exact document. Jul 1, 2013 at 21:59
• @AndrásSalamon As far as I can tell, Nicholas' answer should take care of this question here? Jul 16, 2013 at 10:27

From mcdowella's answer to IcySnow's question Expectation Maximization coin toss examples, Michael Collins' EM Algorithm (pdf) paper presets the "Three Coins Example" in section 3.1 (pdf page 8-9) and explains the steps for recalculating the parameters $\lambda, p_1, p_2$ in accordance with the EM Algorithm.