Based on reproducing kernel theory, an effective numerical technique is proposed for solving second order linear two-point boundary value problems with deviating argument. In this method, reproducing kernels with Chebyshev polynomial form are used (C-RKM). The convergence and an error estimation of the method are discussed. The efficiency and the accuracy of the method is demonstrated on some n...

A short review of diffusion parameter estimations methods from discrete observations is presented. The applicability of a new estimation method on inferences about a diffusion growth model is discussed.

We study the temperature profile for a semi-infinite thin layer due to a Gaussian heat source. We follow the time-dependent temperature profile at the center of the top surface of the layer, deriving the asymptotic behavior and both the short time and long time behaviors. We also show that one can estimate thermal properites from various time measurements of the temperature at the center of the...

This paper provides an overview of Linear Optimality Theory (LOT), a variant of Optimality Theory (OT) designed for the modeling of gradient acceptability judgment data. We summarize the empirical properties of gradient data that have been reported in the experimental literature, and use them to motivate the design of LOT. We discuss LOT’s notions of constraint competition and optimality, as we...

The ARTFIMA model applies a tempered fractional difference to the standard ARMA time series. This paper develops parameter estimation methods for the ARTFIMA model, and demonstrates a new R package. Several examples illustrate the utility of the method.

In contrast to previous analyses, we demonstrate a Bayesian approach to the estimation of the CKM phase α that is invariant to parameterization. We also show that in addition to computing the marginal posterior in a Bayesian manner, the distribution must also be interpreted from a subjective Bayesian viewpoint. Doing so gives a very natural interpretation to the distribution. We also comment on...

This paper considers the problem of estimation and identification with bounded uncertainties in the data and system matrices. In particular, we will examine how to solve the Ax ≈ b when we have bounds on the errors of column blocks of A in the min max sense. Error bounds on column blocks of the system matrix A are caused by numerous situations, such as different sensor or parameter uncertaintie...