Table 9 A list of terms and their meaning
Variable | Meaning |
|---|---|
NB | Naïve Bayes Classifier |
\(RSS\) | Received signal strength or detection power, model parameter |
\(HR\) | Hit ratio, model parameter |
\(CRL\) | Consecutive record length, model parameter |
\(NR\) | Noise ratio, model parameter |
\({\delta }^{2}L\) | Difference in time-lab between detections, model parameter |
\(PDH\) | Proximate detection history |
\(P\left({C}_{i}|{F}_{1},\dots ,{F}_{n}\right)\) | Posterior probability of a detection belonging to detection class \({C}_{i}\) given the observed predictor variables \({F}_{1},\dots ,{F}_{n}.\) |
\(P({C}_{i})\) | Prior probability of the ith detection class occurring where (\(C\in \left\{\mathrm{Valid},\mathrm{False Positive}\right\}\)) |
\(P({F}_{j}|{C}_{i})\) | Likelihood (conditional probability) of the jth observed predictor (\({F}_{j}\)) value given the ith detection class (\({C}_{i}\)) |
\({t}_{p}\) | True positive |
\({f}_{p}\) | False positive |
\({f}_{n}\) | False negative |
\({t}_{n}\) | True negative |
\(sen\) | Sensitivity, model metric, \(sen={t}_{p}/({t}_{p}+{f}_{n})\) |
\(spc\) | Specificity, model metric,\(spc={t}_{n}/({f}_{p}+{t}_{n})\) |
\(npv\) | Negative predictive value, model metric,\(npv={t}_{n}/({f}_{n}+{t}_{n})\) |
\(ppv\) | Positive predictive value, model metric,\(ppv={t}_{p}/({t}_{p}+{f}_{p})\) |
\(fpr\) | False positive rate, model metric,\(fpr={f}_{p}/({f}_{p}+{t}_{n})\) |
PRC | Precision-recall curve |
AUC | Area under the curve statistic, integral of the PRC |
\(\kappa\) | Cohen’s Kappa measure of concordance |