In fact, mixture weights are estimated as ML or MAP estimated values in categorical QLMs.
in ML estimation,α m estimated from document D is determined asα ML estimated generative probability of w in the hyperspherical QLMs is formulated as where v is a normalized word embedding of v. in MAP estimation,α m estimated from document D is determined aŝ where C is document collection in which all retrieved documents are included.