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[Question] What is the difference between predict_proba and log_probability methods for HMMs #1089
Comments
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Thanks for the reply. I am trying to understand the physical meaning of the results from the |
I think you're entering one of the confusing areas of probability theory. Basically, just because a point estimate is above 1 doesn't mean that it's guaranteed to happen. For instance, in my example above, P(0.0001) would be above 1 but so would P(0.00011). Both can't be guaranteed to happen. Instead, people usually look at probabilites of events happening within ranges of a probability distribution and then set those ranges to be very small, e.g., (P(x+e) - P(x - e)) / 2e In my experience, the most practical interpretation of probabilities greater than 1 is that your model has overfit to something. |
Understood. Thanks for the clarification. What do you recommend to reduce overfitting for HMMs? |
I am fitting/training HMMs using time series (datetime) data transformed into radial basis functions or sine/cosine vectors scaled using min-max scaler. However, I keep obtaining positive
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Hello,
I fitted an HMM to a set of observation sequences, however I get positive log probability values (or probability values greater than 1) when I call the
log_probability
method on some test observation sequences. What does positivelog probability
values mean in the context of the HMM inference, and how is thelog_probability
method different from thepredict_proba
method?The text was updated successfully, but these errors were encountered: