LDAPrototype: a model selection algorithm to improve reliability of latent Dirichlet allocation.

Latent Dirichlet allocation (LDA) is a popular method for analyzing large text corpora, but it suffers from instability due to its reliance on random initialization. This results in different outcomes for replicated runs, hindering reproducibility. To address this, we introduce LDAPrototype, a new approach for selecting the most representative LDA run from multiple replications on the same dataset.

On the assessment of the ability of measurements, nowcasts, and forecasts to track changes.

Background: Measurements, nowcasts, or forecasts ideally should correctly reflect changes in the values of interest. In this article, we focus on how to assess the ability of measurements, nowcasts, or forecasts to correctly predict the direction of changes in values - which we refer to as the ability to track changes (ATC).

Methods: We review and develop visual techniques and quantitative measures to assess ATC.