Gaussian Mixture Models are a probabilistic model that assumes that the data is generated from a mixture of several Gaussian distributions, each representing different clusters or groups within the data. This approach allows for the modeling of complex, multi-modal distributions, making it particularly useful for tasks such as classification and density estimation. In the context of analyzing time series data for damage detection, GMM can effectively capture underlying patterns and anomalies in the data over time.
congrats on reading the definition of Gaussian Mixture Models (GMM). now let's actually learn it.