An Outline of the Bayesian Decision Theory

In this paper we give an outline on the Bayesian Decision Theory.Comment: arXiv admin note: text overlap with arXiv:1409.826

Prediction of Complex Systems Using Grey Models

Complexity is an inherent property of the world known. According to Kolmogoroff Randomness and Complexity are connected. Therefore the description of randomness using stochastical procedures has been widely used. Nevertheless other methods might be used to predict complex systems, such as Grey Models. In this paper the occurrence of extreme water levels along the Dutch north-sea has been investigated using Grey Models. Other applications are possible and have been carried out by the authors, such as identification of damaged elements in reinforced concrete structural elements

Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community

Stroke risk in patients with device-detected atrial high-rate episodes

Cardiovascular implantable electronic devices (CIEDs) can detect atrial arrhythmias, i.e. atrial high-rate episodes (AHRE). The thrombo-embolic risk in patients showing AHRE appears to be lower than in patients with clinical atrial fibrillation (AF) and it is unclear whether the former will benefit from oral anticoagulants. Based on currently available evidence, it seems reasonable to consider antithrombotic therapy in patients without documented AF showing AHRE >24 hours and a CHA(2)DS(2)-VASc score (congestive heart failure, hypertension, age >= 75 years [doubled], diabetes mellitus, prior stroke [doubled], vascular disease, age 65-74 years and female sex) >= 1, awaiting definite answers from ongoing randomised clinical trials. In patients with AHR

Gut-microbe derived TMAO and its association with more progressed forms of AF:Results from the AF-RISK study

Introduction: The importance of gut microbiome in cardiovascular disease has been increasingly recognized. Trimethylamine N-oxide (TMAO) is a gut microbe-derived metabolite that is associated with cardiovascular disease, including atrial fibrillation (AF). The role of TMAO in clinical AF progression however remains unknown. Methods and results: In this study we measured TMAO and its precursor (betaine, choline, and L- carnitine) levels in 78 patients using plasma samples from patients that participated in the AF-RISK study. 56 patients suffered from paroxysmal AF and 22 had a short history of persistent AF. TMAO levels were significantly higher in patients with persistent AF, as compared to those with paroxysmal AF (median [IQR] 5.65 [4.7–9.6] m/z versus 4.31 [3.2–6.2] m/z, p < 0.05), while precursor levels did not differ. In univariate analysis, we observed that for every unit increase in TMAO, the odds for having persistent AF increased with 0.44 [0.14–0.73], p < 0.01. Conclusion: These results suggest that higher levels of TMAO are associated with more progressed forms of AF. We therefore hypothesize that increased TMAO levels may reflect disease progression in humans. Larger studies are required to validate these preliminary findings.Trial Registration number: Clinicaltrials.gov NCT01510210