Compound oxidized styrylphosphine

A process is described for preparing flame resistant, nontoxic vinyl polymers which contain phosphazene groups and which do not emit any toxic or corrosive products when they are oxidatively degraded. Homopolymers, copolymers, and terpolymers of a styrene based monomer are prepared by polymerizing at least one oxidized styrylphosphine monomer from a group of organic azides, or by polymerizing p-diphenylphosphinestyrene and then oxidizing that monomer with an organoazide from the group of (C6H5)2P(O)N3, (C6H5O)2P(O)N3, (C6H5)2C3N3(N3), and C6H5C3N3(N3)2. Copolymers can also be prepared by copolymerizing styrene with at least one oxidized styrylphosphine monomer

Hierarchical Compound Poisson Factorization

Non-negative matrix factorization models based on a hierarchical Gamma-Poisson structure capture user and item behavior effectively in extremely sparse data sets, making them the ideal choice for collaborative filtering applications. Hierarchical Poisson factorization (HPF) in particular has proved successful for scalable recommendation systems with extreme sparsity. HPF, however, suffers from a tight coupling of sparsity model (absence of a rating) and response model (the value of the rating), which limits the expressiveness of the latter. Here, we introduce hierarchical compound Poisson factorization (HCPF) that has the favorable Gamma-Poisson structure and scalability of HPF to high-dimensional extremely sparse matrices. More importantly, HCPF decouples the sparsity model from the response model, allowing us to choose the most suitable distribution for the response. HCPF can capture binary, non-negative discrete, non-negative continuous, and zero-inflated continuous responses. We compare HCPF with HPF on nine discrete and three continuous data sets and conclude that HCPF captures the relationship between sparsity and response better than HPF.Comment: Will appear on Proceedings of the 33 rd International Conference on Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 4

Compound Poisson and signed compound Poisson approximations to the Markov binomial law

Compound Poisson distributions and signed compound Poisson measures are used for approximation of the Markov binomial distribution. The upper and lower bound estimates are obtained for the total variation, local and Wasserstein norms. In a special case, asymptotically sharp constants are calculated. For the upper bounds, the smoothing properties of compound Poisson distributions are applied. For the lower bound estimates, the characteristic function method is used.Comment: Published in at http://dx.doi.org/10.3150/09-BEJ246 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

A class of CTRWs: Compound fractional Poisson processes

This chapter is an attempt to present a mathematical theory of compound fractional Poisson processes. The chapter begins with the characterization of a well-known L\'evy process: The compound Poisson process. The semi-Markov extension of the compound Poisson process naturally leads to the compound fractional Poisson process, where the Poisson counting process is replaced by the Mittag-Leffler counting process also known as fractional Poisson process. This process is no longer Markovian and L\'evy. However, several analytical results are available and some of them are discussed here. The functional limit of the compound Poisson process is an $\alpha$-stable L\'evy process, whereas in the case of the compound fractional Poisson process, one gets an $\alpha$-stable L\'evy process subordinated to the fractional Poisson process.Comment: 23 pages. To be published in a World Scientific book edited by Ralf Metzle