nalytic
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models
Krishnamurthy, Akshay, Mazumdar, Arya, McGregor, Andrew, Pal, Soumyabrata
We present two different approaches for parameter learning in several mixture models in one dimension. Our first approach uses complex-analytic methods and applies to Gaussian mixtures with shared variance, binomial mixtures with shared success probability, and Poisson mixtures, among others. An example result is that $\exp(O(N^{1/3}))$ samples suffice to exactly learn a mixture of $k