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Found 5 papers, 3 papers with code
Dependent Cluster Mapping (DCMAP): Optimal clustering of directed acyclic graphs for statistical inference
A Directed Acyclic Graph (DAG) can be partitioned or mapped into clusters to support and make inference more computationally efficient in Bayesian Network (BN), Markov process and other models.
A stochastic SIR model for the analysis of the COVID-19 Italian epidemic
We propose a stochastic SIR model, specified as a system of stochastic differential equations, to analyse the data of the Italian COVID-19 epidemic, taking also into account the under-detection of infected and recovered individuals in the population.
Protecting Classifiers From Attacks. A Bayesian Approach
Classification problems in security settings are usually modeled as confrontations in which an adversary tries to fool a classifier manipulating the covariates of instances to obtain a benefit.
Adversarial classification: An adversarial risk analysis approach
Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit.
Computationally Efficient Simulation of Queues: The R Package queuecomputer
Approximate Bayesian computation could offer a straight-forward way to infer parameters for such networks if we could simulate data quickly enough.
Computation Optimization and Control
