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Item type:Publication, Modelling death rates due to COVID-19: A Bayesian approach(Cornell University, 2020-04-06)Objective: To estimate the number of deaths in Peru due to COVID-19. Design: With a priori information obtained from the daily number of deaths due to CODIV-19 in China and data from the Peruvian authorities, we constructed a predictive Bayesian non-linear model for the number of deaths in Peru. Exposure: COVID-19. Outcome: Number of deaths. Results: Assuming an intervention level similar to the one implemented in China, the total number of deaths in Peru is expected to be 612 (95%CI: 604.3 - 833.7) persons. Sixty four days after the first reported death, the 99% of expected deaths will be observed. The inflexion point in the number of deaths is estimated to be around day 26 (95%CI: 25.1 - 26.8) after the first reported death. Conclusion: These estimates can help authorities to monitor the epidemic and implement strategies in order to manage the COVID-19 pandemic. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Screening for aberrant school performances in high-stakes assessments using in influential analysis(Inderscience Publishers, 2020-01-01)A method is proposed to screen for aberrant school performances in large-scale, high-stakes assessments using influential analysis under a Bayesian approach. Proportions of low and high achievers within a school were modelled via the beta inflated mean regression model (Bayes and Valdivieso, 2016) using school performances in previous years as predictors. The general measure of ϕ-divergence proposed by Peng and Dey (1995) was used to determine aberrancy. A simulation study revealed that the method could recover previously distorted school performances as aberrant. The proposed technique was applied to a Peruvian national reading assessment in grade 4th of primary education for which the government provided a school performance incentive bonus. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bayesian nonparametric bivariate survival regression for current status data(International Society for Bayesian Analysis, 2024-01-01)We consider Bayesian nonparametric inference for event time distributions based on current status data. We show that under dependent censoring conventional mixture priors, including the popular Dirichlet process mixture prior, lead to biologically uninterpretable results as they unnaturally skew the probability mass for the event times toward the extremes of the observed data. Simple assumptions on dependent censoring can fix the problem. We then extend the discussion to bivariate current status data with partial ordering of the two outcomes. In addition to dependent censoring, we also exploit some minimal known structure relating the two event times. We design a Markov chain Monte Carlo algorithm for posterior simulation. Applied to a recurrent infection study, the method provides novel insights into how symptoms-related hospital visits are affected by covariates. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, BAYESIAN PHYLOGENETICS FOR LANGUAGE PREHISTORY-AND ARCHAEOLOGY(Oxford University Press, 2025-07-22)Among various approaches to correlating archaeology and language(s), Bayesian language phylogenetics is highly promising in principle—yet controversial in practice. It can provide a framework of a language family's divergence phases, and aspires to estimate their chronology, to help identify which processes in the archaeological record best correspond. Results have been dogged by inconsistency and artefacts, however, so some cautionary tales here first identify past failings, and new solutions. We review the whole pipeline from raw language data through encoding to Bayesian phylogenetic analysis and results. We then focus on how to interpret those results specifically against archaeology. We assess what splits in the trees actually mean; the pros and cons of ancient and proto-languages; and signal on deep prehistory. Throughout, we frankly assess the state of the art, and explore how Bayesian phylogenetic models might be more closely tailored to how descent with modification applies specifically to language families.Scopus© Citations 2 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bayesian Calibration of a 2d Hydraulic Model Using a Convolutional Neural Network Emulator(RELX Group (Netherlands), 2025-01-01)6