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Planning> Sequencer Guess Slip
Expectations for Guess and Slip Updates
Bayes
The best formulation is likely a Bayesian update, given that the other parameters also update using Bayes.
post = prior * likelihood / normal
likelihood / normal
is the Bayes factor, determines how much the probability changes per update.
If likelihood / normal
is 1, then there is no change in probability. If l / n
is greater than 1, then the probability increases. If l / n
is less than 1, then the probability decreases.
Either the likely or the normal will include addition or subtraction, because without it the numbers would just cancel out through division.
If l / n
is negative or zero, probability drops to zero or less than zero.
If n / l < p
, then the probability goes above one.
Error Rate
Error rates can be measured as
error = sqrt( sum((result  expectation)^2 for each instance) / num_instances )
The guess and slip update formulas should vastly beat a static guess of the guess slip and mean guess. Given a range of 0.01 > 0.5, the error of the control is approximately 0.145.
The error should decrease with fewer cards being evaluated and more learner data available.
Preferably, the error rate would be in the range of a few hundredths, meaning 0.05 or less.
Parameters
Parameters available for update include score and priors: guess, slip, learned, and transit.
A few notable formulas:
p(answer is a slip  score == 0) = learned 
p(answer is a slip  score == 1) = 0 
p(answer is a slip  score) = learned * (1  score) 
p(answer is a guess  score == 1) = 1  learned 
p(answer is a guess  score == 0) = 0 
p(answer is a guess  score) = (1  learned) * score 
p(correct  learned == 1) = 1  slip 
p(correct  learned == 0) = guess 
p(correct  learned) = learned _ (1  slip) + (1  learned) _ guess 
p(incorrect  learned == 1) = slip 
p(incorrect  learned == 0) = 1  guess 
p(incorrect  learned) = learned _ slip + (1  learned) _ (1  guess) 
p(correct  guess, learned) = learned + (1  learned) _ guess 
p(incorrect  guess, learned) = (1  learned) _ (1  guess) 
p(correct  slip, learned) = learned _ (1  slip) 
p(incorrect  slip, learned) = learned _ slip + (1  learned) 
If a learned answers correctly, guess should go up or stay the same (l / n >= 1
), and slip should go down or stay the same (l / n <= 1
).
If the learner answers incorrectly, guess should go down or stay the same (l / n <= 1
), and slip should go up or stay the same (l / n >= 1
).
A learner with a low learned
tells us more about guess than a learner with a high learned
when the answer is correct.
A learner with a high learned
tells us more about slip than a learner with a low learned
when the answer is incorrect.
Question: Negative cases… How does learned
impact guess when answer is incorrect? How does learned
impact slip when answer is correct? One of three possibilities: no impact, forward (low l, 0 == high impact on guess), inverse (high l, 0 == high impact on guess).
learned  score  guess  slip ———+——+——+—— low  0  ?  + mid  0  ?  ++ high  0  ?  +++ low  1  +++  ? mid  1  ++  ? high  1  +  ?
References
http://stats.stackexchange.com/questions/13275/bayesianprobability1isitpossible
http://en.wikipedia.org/wiki/Bayesian_inference#Probability_of_a_hypothesis