  Count or non-negative data are often log transformed to improve
heteroscedasticity and scaling. To avoid undefined values where the data are
zeros, a small pseudocount (e.g. 1) is added across the dataset prior to
applying the transformation. This pseudocount considers neither the measured
object's a priori abundance nor the confidence with which the measurement was
made, making this practice convenient but statistically unfounded. I introduce
here the latent logarithm, or lag. lag assumes that each observed measurement
is a noisy realization of an unmeasured latent abundance. By taking the
logarithm of this learned latent abundance, which reflects both sampling
confidence/depth and the object's a priori abundance, lag provides a
probabilistically coherent, stable, and intuitive alternative to the
questionable, but conventional "log($x$ + pseudocount)."
