Rotational transformations have traditionally played a key role in enhancing the interpretability of factor analysis via post-hoc modifications of the factor model orientation. Regularization methods also serve to achieve this goal by prioritizing sparse loading matrices. In this work, we cross-fertilize these two paradigms within a unifying Bayesian framework. Our approach deploys intermediate...

Let E be a nite set equipped with a group G of bijective transformations and suppose that X is an irreducible Markov chain on E that is equivariant under the action of G. In particular, if E = G with the corresponding transformations being left or right multiplication, then X is a random walk on G. We show that when X is started at a xed point there is a stopping time U such that the distributi...

To solve image mosaicing problem, this work first use the Harris-Laplace method to first find scaleinvariant keypoints. Through Gaussian-weighting the images intensities with a square window around each keypoint, it is possible to compute a finger print (descriptor) for each keypoint using the same method as the used in SIFT. Ransac, together with DLT algorithm, is used to robust estimate the p...

Starting with a general definition of the Laplace transform on arbitrary time scales, we specify the Laplace transform on isolated time scales, prove several properties of the Laplace transform in this case, and establish a formula for the inverse Laplace transform. The concept of convolution is considered in more detail by proving the convolution theorem and a discrete analogue of the classica...

Partial-post Laplace barriers have been postulated as a means to allow electrowetting transport and geometrical reshaping of fluids, followed by the preservation of fluid geometry after the electrowetting voltage is removed. Reported here is the first investigation of Laplace barriers with the arrayed electrodes and splitting/merging transport functions for an electrowetting lab-on-a-chip. Lapl...

We propose a standard approach to make inference for spatial generalized linear mixed models using Laplace approximation. Based on analysis of two datasets previous analysed in literature, we compare our approach with different approaches. The first the rhizoctonia root rot dataset is an example of Binomial SGLMM and the second rongelap dataset is an example of Poisson or Negative Binomial SGLM...

‎This paper presents a new mixture model via considering the univariate skew Laplace distribution‎. ‎The new model can handle both heavy tails and skewness and is multimodal‎. ‎Describing some properties of the proposed model‎, ‎we present a feasible EM algorithm for iteratively‎ ‎computing maximum likelihood estimates‎. ‎We also derive the observ...

1-type and biharmonic curves by using laplace operator in lorentzian 3-space arestudied and some theorems and characterizations are given for these curves.

small scale thermal devices, such as micro heater, have led researchers to consider more accurate models of heat in thermal systems. moreover, biological applications of heat transfer such as simulation of temperature field in laser surgery is another pathway which urges us to re-examine thermal systems with modern ones. non-fourier heat transfer overcomes some shortcomings of fourier heat tran...

We present a novel method for approximate inference in Bayesian models and regularized risk functionals. It is based on the propagation of mean and variance derived from the Laplace approximation of conditional probabilities in factorizing distributions, much akin to Minka’s Expectation Propagation. In the jointly normal case, it coincides with the latter and belief propagation, whereas in the ...