# How can we model a continuous coin tossing system to predict next tossing result and having a variable bias in the coin [duplicate]

Let's assume we have an unfair coin and a machine that toss it continuously. We counted the number of tandem heads. Whenever it's head we count 1, if it's head again, counter goes to two and so on. When we get our first tail, it reset to zero.

Here's the result of tossing: [H,H,T,H,H,H,T,H,T,T]

Collected data: [1,2,0,1,2,3,0,1,0,0]

The thing I'm interested in is to read the past x readings and predict the next tossing result.

For example, assuming this data, the maximum number or continuous heads in the past 5 readings was 3, how can we translate it to a probability of the next tossing.

• Since you can easily recover the tossing result from the collected data, why do you introduce the complication of the counter?
– whuber
Sep 21, 2018 at 17:28
• I want to use the past distribution of continuous heads to update my assumption on the fairness of the coin. We don't know anything about how unfair the coin is. Sep 21, 2018 at 18:07