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## Main Question or Discussion Point

Let's say I have a series of 100 coin tosses, heads or tails. In fact (for my actual data) I don't know if subsequent trials are correlated or what the actual probabilities of getting heads or tails are. Nevertheless, I want to fit a geometric distribution, which gives me the distribution of the number of tails seen before a head come up.

Now I'm unsure how to actually approach this in practice. Can I take each point in the sequence and calculate how many tails come before a head, or would this overcount by using overlapping sequences. For example 4 heads in a row would be counted once as 4, then as 3 and then as 2 and 1 and 0 if i used this scheme. Alternatively do I take random samples as starting points or do I start each time the series alternates between heads and tails? If the series were uncorrelated (which the geometric distribution models it as) then it shouldnt matter which of these schemes I choose.

Any advice? Thanks.

Now I'm unsure how to actually approach this in practice. Can I take each point in the sequence and calculate how many tails come before a head, or would this overcount by using overlapping sequences. For example 4 heads in a row would be counted once as 4, then as 3 and then as 2 and 1 and 0 if i used this scheme. Alternatively do I take random samples as starting points or do I start each time the series alternates between heads and tails? If the series were uncorrelated (which the geometric distribution models it as) then it shouldnt matter which of these schemes I choose.

Any advice? Thanks.