Search Results for author:
Found 5 papers, 0 papers with code
A Provably Improved Algorithm for Crowdsourcing with Hard and Easy Tasks
After separating tasks by type, any Dawid-Skene algorithm (i. e., any algorithm designed for the Dawid-Skene model) can be applied independently to each type to infer the truth values.
On the Consistency of Maximum Likelihood Estimators for Causal Network Identification
Incoming edges to a node in the graph indicate that the state of the node at a particular time instant is influenced by the states of the corresponding parental nodes in the previous time instant.
Mixing Times and Structural Inference for Bernoulli Autoregressive Processes
For a network with $p$ nodes, where each node has in-degree at most $d$ and corresponds to a scalar Bernoulli process generated by a BAR, we provide a greedy algorithm that can efficiently learn the structure of the underlying directed graph with a sample complexity proportional to the mixing time of the BAR process.
A MAP approach for $\ell_q$-norm regularized sparse parameter estimation using the EM algorithm
In this paper, Bayesian parameter estimation through the consideration of the Maximum A Posteriori (MAP) criterion is revisited under the prism of the Expectation-Maximization (EM) algorithm.
Estimator Selection: End-Performance Metric Aspects
The separation of the system estimator from the experiment design is done within this new framework by choosing and fixing the estimation method to either a maximum likelihood (ML) approach or a Bayesian estimator such as the minimum mean square error (MMSE).
