13 research outputs found
Comparison between Suitable Priors for Additive Bayesian Networks
Additive Bayesian networks are types of graphical models that extend the
usual Bayesian generalized linear model to multiple dependent variables through
the factorisation of the joint probability distribution of the underlying
variables. When fitting an ABN model, the choice of the prior of the parameters
is of crucial importance. If an inadequate prior - like a too weakly
informative one - is used, data separation and data sparsity lead to issues in
the model selection process. In this work a simulation study between two weakly
and a strongly informative priors is presented. As weakly informative prior we
use a zero mean Gaussian prior with a large variance, currently implemented in
the R-package abn. The second prior belongs to the Student's t-distribution,
specifically designed for logistic regressions and, finally, the strongly
informative prior is again Gaussian with mean equal to true parameter value and
a small variance. We compare the impact of these priors on the accuracy of the
learned additive Bayesian network in function of different parameters. We
create a simulation study to illustrate Lindley's paradox based on the prior
choice. We then conclude by highlighting the good performance of the
informative Student's t-prior and the limited impact of the Lindley's paradox.
Finally, suggestions for further developments are provided.Comment: 8 pages, 4 figure
Transport of Po Valley aerosol pollution to the northwestern Alps – Part 1: Phenomenology
Mountainous regions are often considered pristine environments;
however they can be affected by pollutants emitted in more populated and
industrialised areas, transported by regional winds. Based on experimental
evidence, further supported by modelling tools, here we demonstrate and quantify
the impact of air masses transported from the Po Valley, a European
atmospheric pollution hotspot, to the northwestern Alps. This is achieved
through a detailed investigation of the phenomenology of near-range (a few
hundred kilometres), trans-regional transport, exploiting synergies of
multi-sensor observations mainly focussed on particulate matter. The explored
dataset includes vertically resolved data from atmospheric profiling
techniques (automated lidar ceilometers, ALCs), vertically integrated aerosol
properties from ground (sun photometer) and space, and in situ measurements
(PM10 and PM2.5, relevant chemical analyses, and aerosol
size distribution). During the frequent advection episodes from the Po basin,
all the physical quantities observed by the instrumental setup are found to
significantly increase: the scattering ratio from ALC reaches values >30,
aerosol optical depth (AOD) triples, surface PM10 reaches
concentrations >100 µg m−3 even in rural areas, and
contributions to PM10 by secondary inorganic compounds such as
nitrate, ammonium, and sulfate increase up to 28 %, 8 %, and 17 %,
respectively. Results also indicate that the aerosol advected from the Po
Valley is hygroscopic, smaller in size, and less light-absorbing compared to
the aerosol type locally emitted in the northwestern Italian Alps. In this
work, the phenomenon is exemplified through detailed analysis and discussion
of three case studies, selected for their clarity and relevance within the
wider dataset, the latter being fully exploited in a companion paper
quantifying the impact of this phenomenology over the long-term
(Diémoz et al., 2019). For the three case studies investigated, a high-resolution
numerical weather prediction model (COSMO) and a Lagrangian tool (LAGRANTO)
are employed to understand the meteorological mechanisms favouring
transport and to demonstrate the Po Valley origin of the air masses. In
addition, a chemical transport model (FARM) is used to further support the
observations and to partition the contributions of local and non-local
sources. Results show that the simulations are important to the understanding
of the phenomenon under investigation. However, in quantitative terms,
modelled PM10 concentrations are 4–5 times lower than the ones
retrieved from the ALC and maxima are anticipated in time by 6–7 h.
Underestimated concentrations are likely mainly due to deficiencies in the
emission inventory and to water uptake of the advected particles not fully
reproduced by FARM, while timing mismatches are likely an effect of
suboptimal simulation of up-valley and down-valley winds by COSMO. The
advected aerosol is shown to remarkably degrade the air quality of the Alpine
region, with potential negative effects on human health, climate, and
ecosystems, as well as on the touristic development of the investigated area.
The findings of the present study could also help design mitigation
strategies at the trans-regional scale in the Po basin and suggest an
observation-based approach to evaluate the outcome of their implementation.</p
The mirror project: A dog training method based on social learning
In the last few years much attention has been paid to social learning in dogs. In the “Do as I do” (Topál et al., 2006) protocol, behaviors are initially taught by traditional techniques. The aim of the Mirror Project is to suggest a training method where dogs reproduce observed behaviors since the beginning.
The protocol is formed by 7 phases: 1) spontaneous reproduction of 6 behaviors after demonstration; 2) random reproduction of these behaviors; reproduction of: 3) other known behaviors; 4) sequences of known behaviors; 5) unknown behaviors; 6) mixed sequences; 7) different behaviors referred to the same object.
Besides the training process, owners were advised to practice, in daily life, activities thought to be useful in developing dog ability to reproduce observed behaviors: doing things together, encouraging dog interest for owner behaviors, using pointing gestures.
The Mirror Project was carried out on 7 dogs. Its effectiveness has been evaluated by behavioral tests. For all behaviors of each phase, the number of exact reproductions of the observed behavior, on a total of 10 (or 12) repetitions, has been counted. The test was considered successful when at least 75% of behaviors were correctly reproduced.
A low number of test repetitions were needed to reach the success rate. In detail: phase 1 1.4±0.5; phase 2 1.0±0.0; phase 3 1.6±0.7; phase 4 1.5±0.6; phase 5 1.5±0.5; phase 6 1.4±0.4; phase 7 1.7±0.5.
The data suggest that the Mirror Project produced good results, and that the use of social learning should be implemented in dog training