feat: implement human based interfaces - #3
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- Add proper Bradley-Terry MLE with Newton-Raphson and MM algorithms - Newton-Raphson provides analytical covariance from Fisher Information - MM algorithm uses bootstrap resampling for variance estimation - Both support convergence checking and regularization - Add uncertainty quantification throughout the system - FitnessEstimate struct with mean, variance, and 95% CIs - WelfordVariance for numerically stable online variance calculation - Candidates now track fitness_with_uncertainty - Add active learning selection strategies - Sequential: original round-robin behavior - UncertaintySampling: prioritizes high-variance candidates - ExpectedInformationGain: maximizes entropy reduction - CoverageAware: ensures minimum coverage before exploring - Integrate uncertainty into session and algorithm - sync_fitness_estimates() to update candidates from aggregator - candidates_by_uncertainty() for identifying under-evaluated candidates - Selection strategy configurable via builder pattern 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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