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## Latent state: is there a hidden meaning here, or am I just dreaming?






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In almost all reallife scenarios, the variables we are interested is not directly measurable (or are not practical to measure), “hidden” from us. We typically infer them by using “observable” variables. Think about stress. In modern life, stress is an inevitable ingredient. We cannot measure it directly, but make an estimate (modelling) via some observable variables such as sleep patterns, sweating, heart rate etc. This is more or less the case for a majority of medical diagnosis. Such a report would include observed symptoms, treatments applied, additional physiological measurements (e.g. blood analysis), opinions of the doctors, but not the disease itself. It is what is deduced from all this data.






The machine learning models we have covered so far can also be interpreted from this perspective, and as a matter of fact, this is what makes “learning” possible. Even in the simplest models (linear regression, kmeans clustering), we learn the relationship between the observed variables (X) and the hidden variables (such as model weights, cluster stereotypes μ) during the learning process. In a bold summary, we can say that data driven learning perspective is built upon the existence of these latent states. The latent variables do not have to any meaning (such as cluster centres), for humans at least, and can be considered as an alternative feature space where learning is (hopefully) easier.









## Discrete and continuous interpretation of the latent space




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