One of the famous mixture models is mixture of Gaussians. Mixture of Gaussians simply tries to fit several Gaussian distributions to the dataset. The number of the Gaussians are assumed to be known a priory. Each point has a probability for being drawn from a particular Gaussian distribution. This probability is called responsibility associated with a data point. Here is a good figure to explain the idea clearly from C. M. Bishop's book [1].
