[plusepsilon]

Author calls it "Model-Based" in place for Bayesian.

I transitioned from using Bayesian models in academia to using machine learning models in industry. One of the core differences in the two paradigms is the "feel" when constructing models. For a Bayesian model, you feel like you're constructing the model from first principles. You set your conditional probabilities and priors and see if it fits the data. I'm sure probabilistic programming languages facilitated that feeling. For machine learning models, it feels like you're starting from the loss function and working back to get the best configuration.

Much of the underlying machinery behind Bayesian vs. machine learning models is the same. Hidden Markov Models are Hidden Markov Models whether they have a prior or not. But this difference in feel influences how you build models and hence, the results.

Now that optimization algos for Bayesian models are catching up, Bayesian ML might become a thing.

Cool stuff.

[dj-wonk]

The blog post author (Daniel Emaasit) wasn't the first to use the "model-based machine learning" phrase. He cites "Model-based machine learning" by Christopher M. Bishop.

[jimbokun]

What do you mean by "machine learning models", as distinct from Bayesian models?

In the literature, Bayesian models are usually considered a type of machine learning model.

[apathy]

Think Bayesian vs frequentist. Obviously from the perspective of "optimize against a loss function" they're both special cases, but one is mathematically and philosophically parsimonious, the other... Less so.