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Worth a read just for the clearest description of differential geometry I’ve ever read (first ten pages). I don’t think you could learn it from the intro, but

Worth a read just for the clearest description of differential geometry I’ve ever read (first ten pages). I don’t think you could learn it from the intro, but you can learn why you might want to know it if you have a “basic mathematical sophistication”. The only thing I remember about Information Geometry in a practical sense was it was related to EM (Expectation-Maximization) algorithms in some way. Most of the associated information geometry research was done in Japan, so logical that Sony would have an interest. Information geometry sounds like a “theory of everything” sort of abstraction that either should lead to breakthrough insights, or just be a forensic sort of postgame wrap-up explaining what’s really going on in that most practical yet ugliest of all mathematical disciplines, statistics. What I remember seemed to me to be a lot of “Adventures in Dualities”, so your mind has to be comfortable with that. Then again, who needs recreational drugs when you have pure math? Wonder if DEA has considered that motto.

pmiller2 6y ago
> Worth a read just for the clearest description of differential geometry I’ve ever read (first ten pages). I don’t think you could learn it from the intro, but you can learn why you might want to know it if you have a “basic mathematical sophistication”. I'm going to read at least the first part because of this comment. Thanks.
webmaven 6y ago
> The only thing I remember about Information Geometry in a practical sense was it was related to EM (Expectation-Maximization) algorithms in some way. Most of the associated information geometry research was done in Japan, so logical that Sony would have an interest. Hmm. Expectation Maximization, as opposed to Regret Minimization?
- kgwgk 6y ago
  https://en.wikipedia.org/wiki/Expectation–maximization_algorithm https://en.wikipedia.org/wiki/Expectation–maximization_algor... The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

pmiller2 6y ago

> Worth a read just for the clearest description of differential geometry I’ve ever read (first ten pages). I don’t think you could learn it from the intro, but you can learn why you might want to know it if you have a “basic mathematical sophistication”. I'm going to read at least the first part because of this comment. Thanks.

webmaven 6y ago

> The only thing I remember about Information Geometry in a practical sense was it was related to EM (Expectation-Maximization) algorithms in some way. Most of the associated information geometry research was done in Japan, so logical that Sony would have an interest. Hmm. Expectation Maximization, as opposed to Regret Minimization?

kgwgk 6y ago
https://en.wikipedia.org/wiki/Expectation–maximization_algorithm https://en.wikipedia.org/wiki/Expectation–maximization_algor... The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.