Efficient estimate of Bayes factors from Reversible Jump output

We exend Meng and Wong (1996) identity from a fixed to a varying dimentional setting. The identity is a very powerful tool to estimate ratios of normalizing constants and thus can be used to evaluate Bayes factors. The extention is driven by the reversibler jump algorithm so that the output from the semplar can be directly used to efficiently estimate the required Bayes factor. Two applications, involving linear and logistic regression models, illustrate the advantages of the suggested approach with respect to alternatives previously proposed in the literature.Bayes factor; Bayesian modeel choice; Marginal likelihood; Markov chain Monte Carlo; Reversible jump

Bayesian inference for Latent Class model via MCMC with application to capture-recapture data

In this paper we propose a Bayesian Latent Class model for capture-recapture data. Through two appliations, the first concerning a sample of snowshoe hares and the second concerning a sample of diabetics in a small Italian town, we show how the proposed approach may be effectively used to obtain point estimates and credibility intervals for the size of a closed-population. To estimate the model we use the Reversible Jump algorithm and the Delayed Rejection strategy to improve its efficiency.Bayesian Inference; Capture-recapture; Delayed Rejection; Latent Class model; Reversible Jump.

Characterisation of beam driven ionisation injection in the blowout regime of Plasma Acceleration

Beam driven ionisation injection is characterised for a variety of high-Z dopant. We discuss the region of extraction and why the position where electrons are captured influences the final quality of the internally-injected bunch. The beam driven ionisation injection relies on the capability to produce a high gradient fields at the bubble closure, with magnitudes high enough to ionise by tunnelling effect the still bounded electrons (of a high-Z dopant). The ionised electrons are captured by the nonlinear plasma wave at the accelerating and focusing wake phase leading to high-brightness trailing bunches. The high transformer ratio guarantees that the ionisation only occurs at the bubble closure. The quality of the ionisation-injected trailing bunches strongly and non-linearly depends on the properties of the dopant gas (density and initial ionisation state). We use the full 3D PIC code ${\tt ALaDyn}$ to consider the highly three-dimensional nature of the effect. By means of a systematic approach we have investigated the emittance and energy spread formation and the evolution for different dopant gases and configurations

Accounting for Connectivity Uncertainties in Predicting Roadkills: a Comparative Approach between Path Selection Functions and Habitat Suitability Models

Functional connectivity modeling is increasingly used to predict the best spatial location for over- or underpasses, to mitigate road barrier effects and wildlife roadkills. This tool requires estimation of resistance surfaces, ideally modeled with movement data, which are costly to obtain. An alternative is to use occurrence data within species distribution models to infer movement resistance, although this remains a controversial issue. This study aimed both to compare the performance of resistance surfaces derived from path versus occurrence data in identifying road-crossing locations of a forest carnivore and assess the influence of movement type (daily vs. dispersal) on this performance. Resistance surfaces were built for genet (Genetta genetta) in southern Portugal using path selection functions with telemetry data, and species distribution models with occurrence data. An independent roadkill dataset was used to evaluate the performance of each connectivity model in predicting roadkill locations. The results show that resistance surfaces derived from occurrence data are as suitable in predicting roadkills as path data for daily movements. When dispersal was simulated, the performance of both resistance surfaces was equally good at predicting roadkills. Moreover, contrary to our expectations, we found no significant differences in locations of roadkill predictions between models based on daily movements and models based on dispersal. Our results suggest that species distribution models are a cost-effective tool to build functional connectivity models for road mitigation plans when movement data are not available

The generalized ratios intrinsic dimension estimator

Modern datasets are characterized by numerous features related by complex dependency structures. To deal with these data, dimensionality reduction techniques are essential. Many of these techniques rely on the concept of intrinsic dimension (id), a measure of the complexity of the dataset. However, the estimation of this quantity is not trivial: often, the id depends rather dramatically on the scale of the distances among data points. At short distances, the id can be grossly overestimated due to the presence of noise, becoming smaller and approximately scale-independent only at large distances. An immediate approach to examining the scale dependence consists in decimating the dataset, which unavoidably induces non-negligible statistical errors at large scale. This article introduces a novel statistical method, Gride, that allows estimating the id as an explicit function of the scale without performing any decimation. Our approach is based on rigorous distributional results that enable the quantification of uncertainty of the estimates. Moreover, our method is simple and computationally efficient since it relies only on the distances among data points. Through simulation studies, we show that Gride is asymptotically unbiased, provides comparable estimates to other state-of-the-art methods, and is more robust to short-scale noise than other likelihood-based approaches