Construction and Stationary Distribution of the Fleming-Viot Process with Viability Selection

This paper provides an explicit construction of the Fleming-Viot process with viability selection in a Bayesian nonparametric framework, and derives its stationary distribution. The measure-valued diffusion is obtained as the infinite population limit of the empirical measures of a semi-Markov process of exchangeable particles. In the limit the stationary distribution is shown to be the two-parameter Poisson-Dirichlet process, also known as the Pitman-Yor process.Fleming-Viot process; semi-Markov process; viability selection; stationary distribution; two-parameter Poisson-Dirichlet process.

Bayesian Nonparametric Construction of the Fleming-Viot Process with Fertility Selection

This paper provides the construction in a Bayesian setting of the Fleming-Viot measurevalued process with diploid fertility selection and highlights new connections between Bayesian nonparametrics and population genetics. Via a generalisation of the Blackwell-MacQueen Polya-urn scheme, a Markov particle process is defined such that the associated process of empirical measures converges to the Fleming-Viot diffusion. The stationary distribution, known from Ethier and Kurtz (1994), is then derived through an application of the Dirichlet process mixture model and shown to be the de Finetti measure of the particle process. The Fleming-Viot process with haploid selection is derived as a special case.Fleming-Viot process; Measure-valued process; Fertility selection; Gibbs sampler; Dirichlet process mixture model; Blackwell-MacQueen urn-scheme

A Super-Conducting Linac Driver for the HFBR

This paper reports on the feasibility study of a proton Super-Conducting Linac (SCL) as a driver gor the High-Flux Breader Reactor (HFBR) at Brookhaven National Laboratory (BNL). The Linac operates in Continuos Wave (CW) mode to produce an average 10 MW of beam power. The Linac energy is 1.0 GeV. The average proton beam intensity is 10 mA.Comment: 3 page

On a Gibbs sampler based random process in Bayesian nonparametrics

We define and investigate a new class of measure-valued Markov chains by resorting to ideas formulated in Bayesian nonparametrics related to the Dirichlet process and the Gibbs sampler. Dependent random probability measures in this class are shown to be stationary and ergodic with respect to the law of a Dirichlet process and to converge in distribution to the neutral diffusion model.Random probability measure; Dirichlet process; Blackwell-MacQueen Pólya urn scheme; Gibbs sampler; Bayesian nonparametrics

K+ → π+vv: First results from the NA62 experiment at CERN:Frascati Physics Series

The decay K+ → π +νν¯, with a very precisely predicted branching ratio of less than 10−10, is one of the best candidates to reveal indirect effects of new physics at the highest mass scales. The NA62 experiment at CERN SPS is designed to measure the branching ratio of the K+ → π +νν¯ with a decay-inflight technique, novel for this channel. NA62 took data in 2016, 2017 and 2018. Statistics collected in 2016 allows NA62 to reach the Standard Model sensitivity for K+ → π +νν¯, entering the domain of 10−10 single event sensitivity and showing the proof of principle of the experiment. The preliminary result on K+ → π +νν¯ from the analysis of the 2016 data set is described.

Geometric Stick-Breaking Processes for Continuous-Time Nonparametric Modeling

This paper is concerned with the construction of a continuous parameter sequence of random probability measures and its application for modeling random phenomena evolving in continuous time. At each time point we have a random probability measure which is generated by a Bayesian nonparametric hierarchical model, and the dependence structure is induced through a Wright-Fisher diffusion with mutation. The sequence is shown to be a stationary and reversible diffusion taking values on the space of probability measures. A simple estimation procedure for discretely observed data is presented and illustrated with simulated and real data sets.Bayesian non-parametric inference, continuous time dependent random measure, Markov process, measure-valued process, stationary process, stick-breaking process

Media devices in pre-school children: the recommendations of the Italian pediatric society

BACKGROUND: Young children are too often exposed to mobile devices (MD) and most of them had their own device. The adverse effects of a early and prolonged exposure to digital technology on pre-school children has been described by several studies. Aim of the study is to analyze the consequences of MD exposure in pre-school children. METHODS: We analyzed the documented effects of media exposure on children's mental and physical health. RESULTS: According to recent studies, MD may interfere with learning, children development, well being, sleep, sight, listening, caregiver-child relationship. DISCUSSION: Pediatricians should be aware of both the beneficial and side effects of MD and give advice to the families, according to children's age. CONCLUSION: In according to literature, the Italian Pediatric Society suggest that the media device exposure in childhood should be modulated by supervisors