Extended Data Fig. 2: Bayesian Information Criterion (BIC) for parametrized Gaussian Mixture models fitted by the expectation-maximization algorithm. | Nature Methods

Extended Data Fig. 2: Bayesian Information Criterion (BIC) for parametrized Gaussian Mixture models fitted by the expectation-maximization algorithm.

From: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Extended Data Fig. 2

Each colored symbol indicates the BIC for a given mixture model with a number of components specified in the x axis. ‘EII’: spherical, equal volume; ‘VII’: spherical, unequal volume; ‘EEE’: ellipsoidal, equal volume, shape, and orientation; ‘EEV’: ellipsoidal, equal volume and equal shape. The dashed light blue line indicates the maximum BIC. The Bayesian Information Criterion is a measure for the comparative evaluation among a finite set of statistical models, the measure is based on maximizing the likelihood function while penalizing for the number of parameters in the models36.

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