Journal: Ecology and Evolution

Article Title: Year‐round weather alters nest‐provisioning rates in a migratory owl

doi: 10.1002/ece3.10333

Figure Lengend Snippet: Bayesian linear model (beta distribution) of the proportion of prey deliveries given by the female throughout the night (top) and throughout the nestling period (bottom). Solid and dashed lines represent the fitted value computed using the mean of posteriors, and shaded ribbons represent the 95% credible interval of posteriors. Wet = green/solid, dry = yellow/dashed, warm = red/dashed, cold = blue/solid.

Article Snippet: To assess the effects of precipitation and temperature on owlet growth, we used a Bayesian linear mixed effects model (Gaussian distribution) with a single changepoint, with nestling age as the fixed effect and mass as the response variable.

Techniques:

Journal: Ecology and Evolution

Article Title: Year‐round weather alters nest‐provisioning rates in a migratory owl

doi: 10.1002/ece3.10333

Figure Lengend Snippet: Bayesian linear mixed effects model (gaussian distribution) of adult female (top) and male (bottom) mass (g) throughout the nestling period. Solid and dashed lines represent the fitted value computed using the mean of posteriors, and shaded ribbons represent the 95% credible interval of posteriors. Wet = green/solid, dry = yellow/dashed, warm = red/dashed, cold = blue/solid.

Article Snippet: To assess the effects of precipitation and temperature on owlet growth, we used a Bayesian linear mixed effects model (Gaussian distribution) with a single changepoint, with nestling age as the fixed effect and mass as the response variable.

Techniques:

Journal: Ecology and Evolution

Article Title: Year‐round weather alters nest‐provisioning rates in a migratory owl

doi: 10.1002/ece3.10333

Figure Lengend Snippet: Bayesian changepoint model with random effects (gaussian distribution) of owlet mass (g) throughout the nestling period. Solid and dashed lines represent the fitted value computed using the mean of posteriors, and shaded ribbons represent the 95% credible interval of posteriors. Wet = green/solid, dry = yellow/dashed, warm = red/dashed, cold = blue/solid.

Article Snippet: To assess the effects of precipitation and temperature on owlet growth, we used a Bayesian linear mixed effects model (Gaussian distribution) with a single changepoint, with nestling age as the fixed effect and mass as the response variable.

Techniques:

Journal: Journal of the American Medical Informatics Association : JAMIA

Article Title: Using statistical anomaly detection models to find clinical decision support malfunctions

doi: 10.1093/jamia/ocy041

Figure Lengend Snippet: Timeline of Anomaly Detection using Six Anomaly Detection Models in Four Rules from BWH

Article Snippet: lists our observation summary. table ft1 table-wrap mode="anchored" t5 Table 2. caption a7 Models Assumptions Potential Applications Poisson Changepoint Poisson distribution Identifies point and mean-shift anomalies ARIMA(Autoregressive Integrated Moving Average) Normal distribution Identifies point and mean-shift anomalies HDC (Hierarchical Divisive Changepoint) Non-parametric Identifies mean-shift anomalies Bayesian Changepoint Normal distribution Identifies point and mean-shift anomalies SHESD (Seasonal Hybrid Extreme Studentized Deviate) Normal distribution Identifies point anomalies EDM (E-Divisive with Median) Non-parametric Identifies mean-shift and mean-drift anomalies Open in a separate window Observations of Models Suitable for Finding Different Types of Anomalies These anomaly detection models, if used appropriately, can identify potential problems with CDS systems.

Techniques:

Journal: Journal of the American Medical Informatics Association : JAMIA

Article Title: Using statistical anomaly detection models to find clinical decision support malfunctions

doi: 10.1093/jamia/ocy041

Figure Lengend Snippet: Observations of Models Suitable for Finding Different Types of Anomalies

Article Snippet: lists our observation summary. table ft1 table-wrap mode="anchored" t5 Table 2. caption a7 Models Assumptions Potential Applications Poisson Changepoint Poisson distribution Identifies point and mean-shift anomalies ARIMA(Autoregressive Integrated Moving Average) Normal distribution Identifies point and mean-shift anomalies HDC (Hierarchical Divisive Changepoint) Non-parametric Identifies mean-shift anomalies Bayesian Changepoint Normal distribution Identifies point and mean-shift anomalies SHESD (Seasonal Hybrid Extreme Studentized Deviate) Normal distribution Identifies point anomalies EDM (E-Divisive with Median) Non-parametric Identifies mean-shift and mean-drift anomalies Open in a separate window Observations of Models Suitable for Finding Different Types of Anomalies These anomaly detection models, if used appropriately, can identify potential problems with CDS systems.

Techniques: