multibin

Multibin is a term used to describe the practice of partitioning a data domain into multiple bins. In statistics and data visualization, binning serves to summarize a distribution by counting observations that fall within predefined intervals. Multibin can refer to univariate binning with several intervals or to binning across multiple variables, i.e., multivariate binning.

As a data summarization technique, multibin reduces detail to reveal structure, trend, and density estimates. The choice of binning scheme affects the resulting representation, influencing peak detection, sparsity handling, and the smoothness of estimated distributions. While binning simplifies data, it also introduces information loss and sensitivity to edge placement.

Common methods for selecting bin edges include equal-width bins, where the data range is divided into intervals of identical length; equal-frequency bins, where each bin contains roughly the same number of observations; and adaptive approaches that tailor bin boundaries to data density, such as rules based on data variance or spread. Boundary handling and inclusivity of bin edges are practical considerations that can affect results.

Multibin is used in various domains, including exploratory data analysis, visualization, and machine learning feature engineering. In image processing, histograms built from multiple color channels or texture features are a form of multibin. In quality control and experimental science, binning helps compare distributions across groups or conditions.

Software libraries implement multibin through histogram routines that accumulate counts per bin and may provide normalization to estimate probabilities or densities. In some contexts, multibin refers to computing several histograms in parallel for different variables or data subsets. Related concepts include histogram, binning, density estimation, and multivariate analysis.