Cross-validatory framework for optimal parameter estimation of KPCA and KPLS models

Smoothing parameter estimation framework for IBM word alignment models

IBM models are important word alignment models in Machine Translation. Based on the Maximum Likelihood Estimation principle to estimate their parameters, the models could easily overfit training data when data are sparse. Even though smoothing is a very popular solution in Language Model, there is still a lack of studies on smoothing for word alignment. In this paper, we propose a framework whi...

A Bayesian Framework for Parameter Estimation in Dynamical Models

Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict exp...

Determination of Optimal ValveTiming for Internal Combustion Engines Using Parameter Estimation Method

Parameter estimation for neuron models

Methods for estimating parameters of the Hindmarsh-Rose (HR) neuron model from a single time series are investigated. Two approaches, (1) synchronization based parameter estimation and (2) adaptive observer, are presented. Both methods are applied to membrane potential data recorded from a single lateral pyloric neuron synaptically isolated from other neurons.

Parameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering

In this short paper, we formulate parameter estimation for finite mixture models in the context of discrete optimal transportation with convex regularization. The proposed framework unifies hard and soft clustering methods for general mixture models. It also generalizes the celebrated k-means and expectation-maximization algorithms in relation to associated Bregman divergences when applied to e...