See my other post. Think of it as a predictive model: you're using part of the data to build a model of your mental state - you then test that model by trying to predict the rest of the data. The smoothed curve you generate represents the probablity of making an entry on a particular day. If you use too many Gaussians, you're overfitting, so you use cross-validation to see if you're doing that.
ISTR coming up with a scoring system for guess-the-probability games, there was a log in it somewhere. Alternatively you could come up with a simple scoring system where you take the dot product of the smoothed graph (discretised into days) and the raw data (again binned into days).
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See my other post. Think of it as a predictive model: you're using part of the data to build a model of your mental state - you then test that model by trying to predict the rest of the data. The smoothed curve you generate represents the probablity of making an entry on a particular day. If you use too many Gaussians, you're overfitting, so you use cross-validation to see if you're doing that.
ISTR coming up with a scoring system for guess-the-probability games, there was a log in it somewhere. Alternatively you could come up with a simple scoring system where you take the dot product of the smoothed graph (discretised into days) and the raw data (again binned into days).