| Fitting a mixture of Gaussians |
| .95 |
| .05 |
| .05 |
| .95 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| The EM algorithm alternates |
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| between two steps: |
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| E-step: Compute the posterior |
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| probability that
each Gaussian |
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| generates each
datapoint. |
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| M-step: Assuming that the data |
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| really was
generated this way, |
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| change the
parameters of |
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| each Gaussian to
maximize |
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| the probability
that it would |
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| generate the data
it is |
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| currently
responsible for. |
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| .5 |
| .5 |
| .5 |
| .5 |
