Optimization algorithms appear in the core calculations of numerous Artificial Intelligence (AI) and Machine Learning methods Engineering Business applications. Following recent works on AI’s theoretical deficiencies, a rigour context for optimization problem black-box objective function is developed. The algorithm stems directly from theory probability, instead presumed inspiration. Thus conve...

We introduce the sparse direct sampling method (DSM) to estimate properties of a region from signals that probe region. demonstrate sparse-DSM on two separate problems: estimating both angle-of-arrival radio wave impinging an array and location shape inhomogeneity scattered acoustic waves. The is qualitative in nature, so it does not require simulation forward problem solve inverse problem. gen...

Non-Gaussian distributions are commonly observed in collisionless space plasmas. Generating samples from non-Gaussian is critical for the initialization of particle-in-cell simulations that investigate their driven and undriven dynamics. To this end, we report a computationally efficient, robust tool, Chebsampling, to sample general distribution functions one two dimensions. This tool based on ...

This paper is devoted to the problem of sampling Gaussian fields in high dimension. Solutions exist for two specific structures of inverse covariance : sparse and circulant. The proposed approach is valid in a more general case and especially as it emerges in inverse problems. It relies on a perturbation-optimization principle: adequate stochastic perturbation of a criterion and optimization of...

Longitudinal epidemiologic studies with irregularly observed categorical outcomes present considerable analytical challenges. Generalized linear models (GLMs) tolerate without bias only values missing completely at random and assume that all observations contribute equally. A triggered sampling study design and an analysis using inverse intensity weights in a GLM offer promise of effectively ad...

Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex and multimodal distributions. Such distributions are often sampled using simulated tempering, which augments the target space with an auxiliary inverse temperature variable. Our method exploits the multinomial probabili...

Longitudinal epidemiologic studies with irregularly observed categorical outcomes present considerable analytical challenges. Generalized linear models (GLMs) tolerate without bias only values missing completely at random and assume that all observations contribute equally. A triggered sampling study design and an analysis using inverse intensity weights in a GLM offer promise of effectively ad...

Emerging data stream management systems approach the challenge of massive data distributions which arrive at high speeds while there is only small storage by summarizing and mining the distributions using samples or sketches. However, data distributions can be “viewed” in different ways. A data stream of integer values can be viewed either as the forward distribution f(x), ie., the number of oc...

The resolution of many large-scale inverse problems using MCMC methods requires a step of drawing samples from a high dimensional Gaussian distribution. While direct Gaussian sampling techniques, such as those based on Cholesky factorization, induce an excessive numerical complexity and memory requirement, sequential coordinate sampling methods present a low rate of convergence. Based on the re...