Abstract A method that estimates invisible cracks from the surface based on deformation measured by digital image correlation (DIC) is being developed. An inverse problem setup to estimate such deformation. Surface deformation, DIC method, contains noise. Inverse problems have ill-conditions. The regularization applied in this study an extension of joint estimation maximum a posteriori (JE-MAP)...

In this paper we unify divergence minimization and statistical inference by means of convex duality. In the process of doing so, we prove that the dual of approximate maximum entropy estimation is maximum a posteriori estimation. Moreover, our treatment leads to stability and convergence bounds for many statistical learning problems. Finally, we show how an algorithm by Zhang can be used to sol...

Batch maximum likelihood (ML) and maximum a posteriori (MAP) estimation with process noise is now more than thirty-five years old, and its use in multiple target tracking has long been considered to be too computationally intensive for real-time applications. While this may still be true for general usage, it is ideally suited for special needs such as bias estimation, track initiation and spaw...

A Maximum a posteriori framework for computing pitch tracks as well as voicing decisions is presented. The proposed algorithm consists of creating a time-pitch energy distribution based on predictable energy that improves on the normalized cross-correlation. A large database is used to evaluate the algorithm’s performance against two standard solutions, using glottal closure instants (GCI) obta...

Approximate model equations often relate given measurements to unknown parameters whose estimate is sought. The Least-Squares (LS) estimation criterion assumes the measured data to be exact, and seeks parameters which minimize the model errors. Existing extensions of LS, such as the Total LS (TLS) and Constrained TLS (CTLS) take the opposite approach, namely assume the model equations to be exa...

A simulated annealing method is presented for Maximum A Posteriori state estimation. Specifically, I consider the problems of smoothing and filtering. A minimax entropy interpretation is proposed to motivate the method.