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I have data coming from a real system taking measurements, and it occasionally gives a number completely wrong (ranging system with multiple returns). I'm using bayesian inference to accumulate measurements and I'd like to incorporate this chance into my model.

Now Bayes theorem gives us $$ P(H|E) = \frac{P(E|H)P(H)}{P(E)}. $$ If $T$ is the event that the observation $E$ is "true" then $$ P(E|H) = P(E|H,T)P(T) + P(E|H,!T)P(!T). $$ So the posterior becomes $$ P(H|E) = \frac{P(H)}{P(E)}\big(P(E|H,T)P(T) + P(E|H,!T)P(!T)\big). $$

In the case that $P(!T)=1$ we would like $$ P(H|E) = \frac{P(H)}{P(E)}\big(0 + P(E|H,!T)\big) =P(H), $$ that is, the posterior is equal to the prior, as we have gained no information. This gets us to $$ \frac{P(E|H)}{P(E)} = 1 $$ which says the probability of the observation is independent of the hypothesis, which is true in the model. But I don't know how to generalise it to the case where $0 < P(T) < 1$ and I think I'm missing something obvious.

Do I just set $$ P(E|H,!T) = P(E)? $$

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The exact formula for the theorem that you used here is a bit misleading unless you have a neat bisection of probabilities into two classes. It's not wrong, but you need to remember to account for all the combinations.

A more general form is:

$$ p(H|E) = \frac {p(E|H)p(H)} {\sum_i { p(E|S_i) } } $$ Where $S_i$ are all the possible realizations of $E_i$ and so $H \in S$.

(an integral form would be even more general, but I do not want to wrestle with MathJax for it).

For $T_{yours} = S = \{0, 1\}$ in the case you wrote up, you wind up with exactly the formula you wrote.

If you had a weird experiment, with $S = \{0, 0, 1\}$ (e.g. you reject any outcome less than 100%), you'll get: $ p(E|H) =\\ = \frac {p(H)} {p(E)} ( (p(E|H, T=1)*(1/3) + p(E|H,T=0)*(1/3) + p(E|H,T=0)*(1/3) ) p(E|H) = \frac {p(H)} {p(E)} ( p(P(E|H, T=1)*(1/3) + p(E|H,T=0)*(2/3) )$

For a simple Bernoulli-distributed binary $T$, the parenthesized sum is: $$ (p(E|H, T=1)*p(T) + p(E|H,T=0)*(1-p(T)) $$

If $S$ is any set of $\alpha$ accept-E and $\beta$ reject-E real-valued distributions of a binary $T$, you get: $$ (p(E|H, T=1)*Beta(\alpha, \beta) + p(E|H,T=0)*(1-Beta(\alpha, \beta)) $$

If you want to go even crazier, and accept, say, $T \in \{0, 1, 2\}$, the sum expands to three terms and you'd have to draw the probabilities to multiply each by from some Categorical distribution.

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