Abstract Motivation Approximate Bayesian computation (ABC) has grown into a standard methodology that manages inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of vector informative statistics summarizing raw data. Furthermore, in almost all existing implementations, tolerance level separates acceptance from rejection simulated parameter values needs to be calibrated. Results We propose conduct likelihood-free...

Simulation-based methods such as approximate Bayesian computation (ABC) are well-adapted to the analysis of complex scenarios populations and species genetic history. In this context, supervised machine learning (SML) provide attractive statistical solutions conduct efficient inferences about scenario choice parameter estimation. The Random Forest methodology (RF) is a powerful ensemble SML algorithms used for classification or regression problems. allows conducting at low computational...

Abstract During the COVID-19 pandemic, many countries implemented international travel restrictions that aimed to contain viral spread while still allowing necessary cross-border for social and economic reasons. The relative effectiveness of these approaches controlling pandemic has gone largely unstudied. Here we developed a flexible network meta-population model compare policies, with focus on evaluating benefit policy coordination. Because country-level epidemiological parameters are...

Dating population divergence within species from molecular data and relating such dating to climatic biogeographic changes is not trivial. Yet it can help formulating evolutionary hypotheses regarding local adaptation future responses changing environments. Key issues include statistical selection of a demographic historical scenario among set possible scenarios, estimation the parameter(s) interest under chosen scenario. Such inferences greatly benefit (a) independent information on rate...

Simulation-based methods such as Approximate Bayesian Computation (ABC) are well adapted to the analysis of complex scenarios populations and species genetic history. In this context, supervised machine learning (SML) provide attractive statistical solutions conduct efficient inferences about scenario choice parameter estimation. The Random Forest methodology (RF) is a powerful ensemble SML algorithms used for classification or regression problems. RF allows conducting at low computational...

Selecting a small set of informative features from large number possibly noisy candidates is challenging problem with many applications in machine learning and approximate Bayesian computation. In practice, the cost computing also needs to be considered. This particularly important for networks because computational costs individual can span several orders magnitude. We addressed this issue network model selection using two approaches. First, we adapted nine feature methods account features....

Approximate Bayesian computation (ABC) is a simulation-based likelihood-free method applicable to both model selection and parameter estimation. ABC estimation requires the ability forward simulate datasets from candidate model, but because sizes of observed simulated usually need match, this can be computationally expensive. Additionally, since inference based on comparisons summary statistics computed data, using expensive lead further losses in efficiency. has recently been applied family...

A bstract Dating population divergence within species from molecular data and relating such dating to climatic biogeographic changes is not trivial. Yet it can help formulating evolutionary hypotheses regarding local adaptation future responses changing environments. Key issues include statistical selection of a demographic historical scenario among set possible scenarios, estimation the parameter(s) interest under chosen scenario. Such inferences greatly benefit new approaches including...

During the COVID-19 pandemic, many countries implemented international travel restrictions that aimed to contain viral spread while still allowing necessary cross-border for social and economic reasons. The relative effectiveness of these approaches controlling pandemic has gone largely unstudied. Here we developed a flexible network meta-population model compare policies, with focus on evaluating benefit policy coordination. Because country-level epidemiological parameters are unknown, they...

Approximate Bayesian computation (ABC) is a simulation-based likelihood-free method applicable to both model selection and parameter estimation. ABC estimation requires the ability forward simulate datasets from candidate model, but because sizes of observed simulated usually need match, this can be computationally expensive. Additionally, since inference based on comparisons summary statistics computed data, using expensive lead further losses in efficiency. has recently been applied family...

Selecting a small set of informative features from large number possibly noisy candidates is challenging problem with many applications in machine learning and approximate Bayesian computation. In practice, the cost computing also needs to be considered. This particularly important for networks because computational costs individual can span several orders magnitude. We addressed this issue network model selection using two approaches. First, we adapted nine feature methods account features....