@@ -19,7 +19,6 @@ In summary, in addition to finding the the hyper-parameters $`\pi, \mu, \Sigma_{
### Pseudo code
Algorithm 1. EM for the Fusion of GMMs
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1. Run K-means clustering on the high-quality data to generate the assignment of the voxels $`R^{(0)}`$.
2. Initialise the means $`\mu_{k}`$, covariances $`\Sigma_{k}`$, and mixing coefficients $`\pi_k`$ using the K-means assignment $`R^{(0)}`$, and evaluate the initial likelihood.
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@@ -33,6 +32,8 @@ Algorithm 1. EM for the Fusion of GMMs