Fundamental Problems of Probabilistic Inference

Fundamental Problems of Probabilistic Inference

Why should you care about sampling if you are a machine learning practitioner? By now, I know many people that do research in ML or play around with machine learning algorithms. Yet, most of them somehow don’t appreciate the fundamental problems that machine learning is built upon, the problems of probabilistic inference.

By now, I know many people that do research in ML or play around with machine learning algorithms. Yet, most of them somehow don’t appreciate the fundamental problems that machine learning is built upon, the problems of probabilistic inference. The point of this article is to maybe turn your attention to questions that you might not have considered when coding machine learning algorithms.

Why do we talk about probability in the first place? Where does the randomness come from? Is there such a thing as random variables really? In the end, we want to predict something relatively concrete, a class label given an image, an optimal action given some kind of state description in a Markov Decision Process. Arguably, there is nothing random about these things. An object is not really with some probability assigned a class label, in a random sense. A cow is not maybe a cow, it is certainly a cow.

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Photo by Jean Carlo Emer on Unsplash

On the other hand, we have problems such as different flavors of unsupervised learning, where we might want to reduce dimensionality of the data, cluster the data, learn a generative model that reflects the probability distribution of the data. All of these flavors can be expressed in terms of probability. But again, assigning a latent low-dimensional representation to a data point is not truly random. We want to map directly an input data point to a latent representation (a cluster, a latent variable of lower dimension).

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