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Found 7 papers, 0 papers with code
Identifiability of Product of Experts Models
We show: (a) When the latents are uniformly distributed, the model is identifiable with a number of observables equal to the number of parameters (and hence best possible).
Identification of Mixtures of Discrete Product Distributions in Near-Optimal Sample and Time Complexity
We consider the problem of identifying, from statistics, a distribution of discrete random variables $X_1,\ldots, X_n$ that is a mixture of $k$ product distributions.
Causal Inference Despite Limited Global Confounding via Mixture Models
A Bayesian Network is a directed acyclic graph (DAG) on a set of $n$ random variables (the vertices); a Bayesian Network Distribution (BND) is a probability distribution on the random variables that is Markovian on the graph.
Hadamard Extensions and the Identification of Mixtures of Product Distributions
The Hadamard Extension of a matrix is the matrix consisting of all Hadamard products of subsets of its rows.
Source Identification for Mixtures of Product Distributions
We give an algorithm for source identification of a mixture of $k$ product distributions on $n$ bits.
The Sparse Hausdorff Moment Problem, with Application to Topic Models
We give an algorithm for identifying a $k$-mixture using samples of $m=2k$ iid binary random variables using a sample of size $\left(1/w_{\min}\right)^2 \cdot\left(1/\zeta\right)^{O(k)}$ and post-sampling runtime of only $O(k^{2+o(1)})$ arithmetic operations.
Learning Arbitrary Statistical Mixtures of Discrete Distributions
We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets.
