The above-ground biomass (AGB) of tropical forests is a crucial variable for ecologists, biogeochemists, foresters and policymakers. Tree inventories are an efficient way of assessing forest carbon stocks and emissions to the atmosphere during deforestation. To make correct inferences about long-term changes in biomass stocks, it is essential to know the uncertainty associated with AGB estimate...

Gibbs sampling is a widely applicable inference technique that can in principle deal with complex multimodal distributions. Unfortunately, it fails in many practical applications due to slow convergence and abundance of local minima. In this paper, we propose a general method of speeding up Gibbs sampling in probabilistic models. The method works by introducing auxiliary variables which represe...

We consider the problem of inference in a probabilistic model for transient populations where we wish to learn about arrivals, departures, and population size over all time, but the only available data are periodic counts of the population size at specific observation times. The underlying model arises in queueing theory (as an Mt/G/∞ queue) and also in ecological models for short-lived animals...

Converting the analogue signal, as captured from a patient, into digital format is known as digitizing, or analogue to digital conversion. This is a vital first step in for digital signal processing. The acquisition of high-quality data requires appropriate choices of system and parameters (sampling rate, anti-alias filter, amplification, number of ‘bits’). Thus tutorial aims to provide a pract...

We tackle the facial landmark localization problem as an inference problem over a Markov Random Field. Efficient inference is implemented using Gibbs sampling with approximated full conditional distributions in a latent variable model. This approximation allows us to improve the runtime performance 1000-fold over classical formulations with no perceptible loss in accuracy. The exceptional robus...

This work presents simple and fast structured Bayesian learning for matrix and tensor factorization models. An unblocked Gibbs sampler is proposed for factorization machines (FM) which are a general class of latent variable models subsuming matrix, tensor and many other factorization models. We empirically show on the large Netflix challenge dataset that Bayesian FM are fast, scalable and more ...

We describe a sampling technique particularly suitable for active vision: Dimensionally-Independent Exponential Mapping (DIEM), in which each dimension of the original data is sampled in an exponentially increasing or decreasing series of steps, with bilateral symmetry about the data mid-point. Multidimensional data sampling is achieved by combining single dimension sampling coordinates. DIEM i...

We introduce three natural and well-defined generalizations of maximal Poisson-disk sampling. The first is to decouple the disk-free (inhibition) radius from the maximality (coverage) radius. Selecting a smaller inhibition radius than the coverage radius yields samples which mix advantages of Poisson-disk and uniform-random samplings. The second generalization yields hierarchical samplings, by ...

This article reviews several basic statistical tools needed for modeling data with sampling weights that are implemented in Mplus Version 3. These tools are illustrated in simulation studies for several latent variable models including factor analysis with continuous and categorical indicators, latent class analysis, and growth models. The pseudomaximum likelihood estimation method is reviewed ...