👉 Mixture math, also known as mixture theory or mixture models, is a statistical approach that combines multiple probability distributions to model complex data. It assumes that the observed data is generated from a mixture of several underlying distributions, each representing a different component or subpopulation within the data. By mixing these components, mixture models can capture intricate patterns and heterogeneity that a single distribution might miss. This method is particularly useful in scenarios where data comes from diverse sources or has multiple modes, allowing for a more nuanced and flexible representation of the underlying data structure. For example, in image segmentation, mixture models can blend different color distributions to accurately separate objects within an image.
