we discuss in this paper the strong convergence for weighted sums of negatively orthant dependent (nod) random variables by generalized gaussian techniques. as a corollary, a cesaro law of large numbers of i.i.d. random variables is extended in nod setting by generalized gaussian techniques.

The goal of this work is the further development of neoclassical analysis, which extends the scope and results of the classical mathematical analysis by applying fuzzy logic to conventional mathematical objects, such as functions, sequences, and series. This allows us to reflect and model vagueness and uncertainty of our knowledge, which results from imprecision of measurement and inaccuracy of...

This work draws on previous works regarding spectral learning algorithm for structured data (see [5], [9], [2], [1], [8]). We present an extension of the Hidden Markov Models, called Graphical Weighted Models (GWM), whose purpose is to model distributions over labeled graphs. We describe the spectral algorithm for GWM, which generalizes the previous spectral algorithms for sequences and trees. ...