Seems a little confused. If 99% refers to a model of the coin, and the larger number equals the conditional probability of tails within that model, then I think P(model) suddenly drops to less than .02%. That assumes the other 1% goes to a uniform prior and we can treat the chance of heads if (not-model) as 50%. In this example I think I could have told you beforehand the model leaves out too much, because my sources say the outcome depends more on how you throw the coin and you won’t ever get 99.9999% from this.
If you want to know the posterior probability of heads or tails look at the other comments.
Seems a little confused. If 99% refers to a model of the coin, and the larger number equals the conditional probability of tails within that model, then I think P(model) suddenly drops to less than .02%. That assumes the other 1% goes to a uniform prior and we can treat the chance of heads if (not-model) as 50%. In this example I think I could have told you beforehand the model leaves out too much, because my sources say the outcome depends more on how you throw the coin and you won’t ever get 99.9999% from this.
If you want to know the posterior probability of heads or tails look at the other comments.