[solidago] feat: Asymetric uncertainty #1781
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lfaucon
commented
Sep 24, 2023
| negative_exponential_term = np.exp((normalized_r_ab - 1) * theta_ab) | ||
| return np.where( | ||
| np.abs(theta_ab) < EPSILON, | ||
| 1 / 2, |
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Probably log of 1/2 instead
| if f(b, *args) == 0: | ||
| return b | ||
| if f(a, *args) * f(b, *args) > 0: | ||
| raise ValueError("Function `f` should have opposite sign on bounds `a` and `b`") |
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optimize by not calling f(a) and f(b) twice
| @@ -39,6 +53,64 @@ def Delta_theta(theta_ab): | |||
| ).sum() ** (-0.5) | |||
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| HIGH_LIKELIHOOD_RANGE_THRESHOLD = 1 | |||
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@lenhoanglnh Do you confirm the idea of using a Likelihood lower bound to compute the uncertainty interval, rather than a more standard 90% confidence interval?
| indices_b, _r_ab = coord_to_subset[idx_a] | ||
| indices_b, r_ab = coord_to_subset[idx_a] | ||
| lower_bound, upper_bound = get_high_likelihood_range( | ||
| continuous_bradley_terry_log_likelihood, |
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@lenhoanglnh When calculating the high likelihood range, should we include only the likelihood of observed comparisons, or also the prior/regularization (something like alpha * theta^2)?
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Replaced by #1973 |
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#1780
Description
Uncertainty of individual scores should be asymmetric.
This PR updates the computation of individual scores for the Continuous Bradley Terry model in solidago using the method of "high likelihood range".
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