  In the literature, finite mixture models are described as linear combinations
of probability distribution functions having the form $\displaystyle f(x) =
\Lambda \sum_{i=1}^n w_i f_i(x)$, $x \in \mathbb{R}$, where $w_i$ are positive
weights, $\Lambda$ is a suitable normalising constant and $f_i(x)$ are given
probability density functions. The fact that $f(x)$ is a probability density
function follows naturally in this setting. Our question is: what happens when
we remove the sign condition on the coefficients $w_i$?
  The answer is that it is possible to determine the sign pattern of the
function $f(x)$ by an algorithm based on finite sequence that we call a
generalized Budan-Fourier sequence. In this paper we provide theoretical
motivation for the functioning of the algorithm, and we describe with various
examples its strength and possible applications.
