A resampling technique for probability-proportional-to size sampling designs is proposed. It essentially based on a special form of variable probability, without replacement applied directly to the sample data, yet according pseudo-population approach. From theoretical point view, it asymptotically correct: as both and population increase, under mild regularity conditions proposed design tends ...

TensorFlow Probability is a powerful library for statistical analysis in Python. Using Probability’s implementation of Bayesian methods, modelers can incorporate prior information and obtain parameter estimates quantified degree belief the results. Resampling methods like Markov Chain Monte Carlo also be used to perform analysis. As an alternative, we show how use numerical optimization estim...

In this work, we propose a method to interactively deform high-resolution volumetric datasets, such as those obtained through medical imaging. Interactive deformation enables the visualization of these datasets in full detail using state-of-the-art volume rendering techniques as they are dynamically modified. Our approach relies on resampling the original dataset to a target regular grid, follo...

Prior studies on converting three-dimensional (3D) range data into regular two-dimensional (2D) color images using virtual fringe projection techniques showed great promise for 3D range data compression, yet they require resampling the raw scanned data. Due to this resampling, the natural 3D range data are altered and sampling error may be introduced. This paper presents a method that compresse...

We propose a novel efficient algorithm for robust tracking of a fixed number of targets in real-time with low failure rate. The method is an instance of Sequential Importance Resampling filters approximating the posterior of complete target configurations as a mixture of Gaussians. Using predicted target positions by Kalman filters, data associations are sampled for each measurement sweep accor...