Abstract Recognition of human actions, under low observational latency, is a growing interest topic, nowadays. Many approaches have been represented based on a provided set of 3D Cartesian coordinates system originated at a certain specific point located on a root joint. In this paper, We will present a statistical detection and recognition system using Hidden Markov Model using 7 types of pose...

A new hierarchical tree-based topic model is developed, based on nonparametric Bayesian techniques. The model has two unique attributes: (i) a child node in the tree may have more than one parent, with the goal of eliminating redundant sub-topics deep in the tree; and (ii) parsimonious sub-topics are manifested, by removing redundant usage of words at multiple scales. The depth and width of the...

We introduce a new type of invariant of block isomorphism for Markov shifts, defined by summing the weights of all spanning trees for a presentation of the Markov shift. We give two proofs of invariance. The first uses the Matrix-Tree Theorem to show that this invariant can be computed from a known invariant, the stochastic zeta function of the shift. The second uses directly the definition to ...

A “lifting” of a Markov chain is a larger chain obtained by replacing each state of the original chain by a set of states, with transition probabilities defined in such a way that the lifted chain projects down exactly to the original one. It is well known that lifting can potentially speed up the mixing time substantially. Essentially all known examples of efficiently implementable liftings ha...

We study the size properties of a general model of fractal sets that are based on a tree-indexed family of random compacts and a tree-indexed Markov chain. These fractals may be regarded as a generalization of those resulting from the Moran-like deterministic or random recursive constructions considered by various authors. Among other applications, we consider various extensions of Mandelbrot's...

MOTIVATION Computationally identifying non-coding RNA regions on the genome has much scope for investigation and is essentially harder than gene-finding problems for protein-coding regions. Since comparative sequence analysis is effective for non-coding RNA detection, efficient computational methods are expected for structural alignments of RNA sequences. On the other hand, Hidden Markov Models...

As Web accesses increase exponentially in the past decade, it is fundamentally important for Web servers to be able to minimize the latency and respond to users’ requests very quickly. One commonly used strategy is to “predict” what pages the user is likely to access in the near future so that the server can prefetch these pages and store them in a cache on the local machine, a Web proxy or a W...

We consider the mean value properties for finite variation measures with respect to a Markov operator in a discrete environnement. We prove equivalent conditions for the weak mean value property in the case of general Markov operators and for the strong mean value property in the case of transient Markov operators adapted to a tree structure. In this last case, conditions for the equivalence be...

One popular class of such models are Markov networks, which use an undirected graph to represent dependencies among variables. Markov networks of low tree-width (i.e. having a triangulation with small cliques ) allow efficient computations, and are useful as learned probability models [8]. A well studied case is that in which the dependency structure is known in advance. In this case the underl...

Markov Blankets discovery algorithms are important for learning a Bayesian network structure. We present an argument that tree ensemble masking measures can provide an approximate Markov blanket. Then an ensemble feature selection method is used to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). We compare our algorithm in the causal stru...