(See other posts for related questions) Q2: A suspicious coin is known as either fair (50/50) or 2-headed. 10 tosses, all heads. What’s the probability of unfair? You could, if you like, imagine that you randomly pick one from a pool of either fair or unfair coins, but you don’t know how many percent of them are 2-headed.

A: We will follow “*a property in common with many priors, namely, that the posterior from one problem becomes the prior for another problem; pre-existing evidence which has already been taken into account is part of the prior and as more evidence accumulates the prior is determined largely by the evidence rather than any original assumption*.” See wikipedia.

F denotes “picked a Fair coin”

U denotes “picked an Unfair”. P(U) == 100% – P(F)

H denotes “getting a Head”

Prior_1: P(F) is assumed to be 50%, my subjective initial estimate, based on zero information.

Posterior_1: P(F|H) is the updated estimate (confidence level) after seeing 1st Head.

P(F|H) =

Assuming P(F) is 50%, then P(F|H) comes to 1/3.

Now this 1/3 is our posterior_1 and also prior_2, to be updated after 2nd head.

P2(F|H) =

Here assuming P(F) = 1/3 and P(U) = 2/3, we get posterior_2 = 1/5 or 20% as the updated estimate after 2nd head.
Q: As shown, posterior_1 (33.33%) is used as P(F) in deriving the updated P(F) (20%), so which value is valid? 33.33% or 20%?

A: both. 33% is valid after seeing first H, and 20% is valid after seeing 2nd H. If 2nd H is declared void, then our best estimate rolls back to 33%. If after 5 heads, first head is declared void, then we should rollback all updates and return to initial prior_1 of 50/50.

Now this posterior_2 is used as prior_3, to be updated after 3rd head.

P3(F|H) =

Posterior_3 comes to 1/9 or 11.11%

Let’s stop after first 2 heads and try another solution to posterior_2.

A denotes “1st toss is Head”

B denotes “2nd toss is Head”

P(F|A)=1/3, based on the same initial estimate.

P(F|AB)=

Q (The key question): what value of P(F) to use? 50% or 1/3?
A: P(F) is the initial estimate, without considering any “news”, so P(F) == 50% i.e. our initial estimate. This 50/50 is __basis of all the subsequent updates__ on the guess. The value of 50/50 is kind of subjective, but once we decide on that value, we can’t change this estimate half way through the successive “updates”.

Each successive update *fundamentally and ultimately relies on* the initial numerical level-of-belief. We __stick to our initial “prior” for ever__, not discarding it after 100 updates. Our updated estimates are valid/reasonable exactly due to validity of our initial estimate.

If we notice a digit is accidentally omitted within initial estimate calc, then all versions of updated estimate become invalid. We correct the initial calc and recalc all updated estimates. Here’s an example of a correction in prior_0 — pool of coins is 99% fair coins, so initial 50/50 seriously underestimates P(F) and overestimates P(Unfair coin picked).

If initial estimate is way off from reality (such as the 99%), will successive updates improve it? (Not sure about other cases) Not in this case. The 50/50 incorrect estimate is Upheld by each successive update.

If any news casts doubts over the latest estimate (rather than the validity of initial estimate), we update latest estimate to derive another posterior, but the posterior estimate is no more valid than the prior. Both have the same level of validity, because posterior is derived using prior.

We need to know X) when to Correct the initial prior (and recalc all posteriors) vs Y) when to apply a news to generate a new posterior. If we receive new information in tweets, very very few tweets are Corrective (X). Most of the news are updaters (Y). Such a *news is NOT a “correction of mistake” or newly discovered fact that threatens to discredit the initial estimate — but more of a ***what-if** scenario. “If first 10 tosses are all heads” then how would you update your estimate from the initial 50/50.

“What-if 11th toss is Tail”? We aren’t justified to discredit initial estimate. We update our 10th estimate to a posterior of P(F) = 100%, but this is as valid as the initial 50/50 estimate. 50/50 remains a valid estimate in the no-info context. When we open the pool we see 2 coins only, one fair one unfair, so our 50/50 is the best prior, and the 11th toss doesn’t threaten its validity. When we know nothing about the pool, 50/50 is a reasonable prior.

P(F|AB) comes to 1/5, since there’s no justification to “correct” initial 50/50 estimate.