Credal Model Averaging: dealing robustly with model uncertainty on small data sets.

Datasets of population dynamics are typically characterized by a short temporal extension. In this condition, several alternative models typically achieve close accuracy, though returning quite different predictions (model uncertainty ). Bayesian model averaging (BMA) addresses this issue by averaging the prediction of the different models, using as weights the posterior probability of the models. However, an open problem of BMA is the choice of the prior probability of the models, which can largely impact on the inferences, especially when data are scarce. We present Credal Model Averaging (CMA), which addresses this problem by simultaneously considering a set of prior probability distributions over the models. This allows to represent very weak prior knowledge about the appropriateness of the different models and also to easily accommodate expert judgments, considering that in many cases the expert is not willing to commit himself to a single prior probability distribution. The predictions generated by CMA are intervals whose lengths shows the sensitivity of the predictions on the choice of the prior over the models

Objective way to support embryo transfer: a probabilistic decision

STUDY QUESTION Is it feasible to identify factors that significantly affect the clinical outcome of IVF-ICSI cycles and use them to reliably design a predictor of implantation? SUMMARY ANSWER The Bayesian network (BN) identified top-history embryos, female age and the insemination technique as the most relevant factors for predicting the occurrence of pregnancy (AUC, area under curve, of 0.72). In addition, it could discriminate between no implantation and single or twin implantations in a prognostic model that can be used prospectively. WHAT IS KNOWN ALREADY The key requirement for achieving a single live birth in an IVF-ICSI cycle is the capacity to estimate embryo viability in relation to maternal receptivity. Nevertheless, the lack of a strong predictor imposes several restrictions on this strategy. STUDY DESIGN, SIZE, DURATION Medical histories, laboratory data and clinical outcomes of all fresh transfer cycles performed at the International Institute for Reproductive Medicine of Lugano, Switzerland, in the period 2006-2008 (n = 388 cycles), were retrospectively evaluated and analyzed. PARTICIPANTS/MATERIALS, SETTING, METHODS Patients were unselected for age, sperm parameters or other infertility criteria. Before being admitted to treatment, uterine anomalies were excluded by diagnostic hysteroscopy. To evaluate the factors possibly related to embryo viability and maternal receptivity, the class variable was categorized as pregnancy versus no pregnancy and the features included: female age, number of previous cycles, insemination technique, sperm of proven fertility, the number of transferred top-history embryos, the number of transferred top-quality embryos, the number of follicles >14 mm and the level of estradiol on the day of HCG administration. To assess the classifier, the indicators of performance were computed by cross-validation. Two statistical models were used: the decision tree and the BN. MAIN RESULTS AND THE ROLE OF CHOICE The decision tree identified the number of transferred top-history embryos, female age and the insemination technique as the features discriminating between pregnancy and no pregnancy. The model achieved an accuracy of 81.5% that was significantly higher in comparison with the trivial classifier, but the increase was so modest that the model was clinically useless for predictions of pregnancy. The BN could more reliably predict the occurrence of pregnancy with an AUC of 0.72, and confirmed the importance of top-history embryos, female age and insemination technique in determining implantation. In addition, it could discriminate between no implantation, single implantation and twin implantation with the AUC of 0.72, 0.64 and 0.83, respectively. LIMITATIONS, REASONS FOR CAUTION The relatively small sample of the study did not permit the inclusion of more features that could also have a role in determining the clinical outcome. The design of this study was retrospective to identify the relevant features; a prospective study is now needed to verify the validity of the model. WIDER IMPLICATIONS OF THE FINDINGS The resulting predictive model can discriminate with reasonable reliability between pregnancy and no pregnancy, and can also predict the occurrence of a single pregnancy or multiple pregnancy. This could represent an effective support for deciding how many embryos and which embryos to transfer for each couple. Due to its flexibility, the number of variables in the predictor can easily be increased to include other features that may affect implantation. STUDY FUNDING/COMPETING INTERESTS This study was supported by a grant, CTI Medtech Project Number: 9707.1 PFLS-L, Swiss Confederation. No competing interests are declare

Binary credal classification under sparsity constraints.

Binary classification is a well known problem in statistics. Besides classical methods, several techniques such as the naive credal classifier (for categorical data) and imprecise logistic regression (for continuous data) have been proposed to handle sparse data. However, a convincing approach to the classification problem in high dimensional problems (i.e., when the number of attributes is larger than the number of observations) is yet to be explored in the context of imprecise probability. In this article, we propose a sensitivity analysis based on penalised logistic regression scheme that works as binary classifier for high dimensional cases. We use an approach based on a set of likelihood functions (i.e. an imprecise likelihood, if you like), that assigns a set of weights to the attributes, to ensure a robust selection of the important attributes, whilst training the model at the same time, all in one fell swoop. We do a sensitivity analysis on the weights of the penalty term resulting in a set of sparse constraints which helps to identify imprecision in the dataset

The Buckland Park air shower array

The new Buckland Park Air Shower Array has been producing analyzed shower data since July 1984. The array is described and some preliminary performance figures are presented

Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. 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Bayesian analysis of Jolly-Seber type models; Incorporating heterogeneity in arrival and departure

We propose the use of finite mixtures of continuous distributions in modelling the process by which new individuals, that arrive in groups, become part of a wildlife population. We demonstrate this approach using a data set of migrating semipalmated sandpipers (Calidris pussila) for which we extend existing stopover models to allow for individuals to have different behaviour in terms of their stopover duration at the site. We demonstrate the use of reversible jump MCMC methods to derive posterior distributions for the model parameters and the models, simultaneously. The algorithm moves between models with different numbers of arrival groups as well as between models with different numbers of behavioural groups. The approach is shown to provide new ecological insights about the stopover behaviour of semipalmated sandpipers but is generally applicable to any population in which animals arrive in groups and potentially exhibit heterogeneity in terms of one or more other processes

Rainfall mediations in the spreading of epidemic cholera

Following the empirical evidence of a clear correlation between rainfall events and cholera resurgence that was observed in particular during the recent outbreak in Haiti, a spatially explicit model of epidemic cholera is re-examined. Specifically, we test a multivariate Poisson rainfall generator, with parameters varying in space and time, as a driver of enhanced disease transmission. The relevance of the issue relates to the key insight that predictive mathematical models may provide into the course of an ongoing cholera epidemic aiding emergency management (say, in allocating life-saving supplies or health care staff) or in evaluating alternative management strategies. Our model consists of a set of dynamical equations (SIRB-like i.e. subdivided into the compartments of Susceptible, Infected and Recovered individuals, and including a balance of Bacterial concentrations in the water reservoir) describing a connected network of human communities where the infection results from the exposure to excess concentrations of pathogens in the water. These, in turn, are driven by rainfall washout of open-air defecation sites or cesspool overflows, hydrologic transport through waterways and by mobility of susceptible and infected individuals. We perform an a posteriori analysis (from the beginning of the epidemic in October 2010 until December 2011) to test the model reliability in predicting cholera cases and in testing control measures, involving vaccination and sanitation campaigns, for the ongoing epidemic. Even though predicting reliably the timing of the epidemic resurgence proves difficult due to rainfall inter-annual variability, we find that the model can reasonably quantify the total number of reported infection cases in the selected time-span. We then run a multi-seasonal prediction of the course of the epidemic until December 2015, to investigate conditions for further resurgences and endemicity of cholera in the region with a view to policies which may bring to the eradication of the disease in Haiti. The projections, although strongly depending on still uncertain epidemiological processes, show an endemic, seasonal pattern establishing in the region, which can be better forestalled by an improvement of the sanitation system only, rather than by vaccination alone. We thus conclude that hydrologic drivers and water resources management prove central to prediction, emergency management and long-term control of epidemic cholera

Credal model averaging of logistic regression for modeling the distribution of marmot burrows

Bayesian model averaging (BMA) weights the inferences produced by a set of competing models, using as weights the models posterior probabilities. An open problem of BMA is how to set the prior probability of the models. Credal model averaging (CMA) is a credal ensemble of Bayesian models, which generalizes BMA by substituting the single prior over the models by a set of priors. The base models of the ensemble are learned in a Bayesian fashion. We use CMA to ensemble base classi ers which are Bayesian logistic regressors, characterized by di erent sets of covariates. CMA returns indeterminate classi cations when the classi cation is prior-dependent, namely when the most probable class depends on the prior probability assigned to the di erent models. We apply CMA for modelling the presence and absence of marmot burrows in an Alpine valley in Italy and show that it compares favorably to BMA