141,736 research outputs found
Latent Gaussian Count Time Series Modeling
This paper develops theory and methods for the copula modeling of stationary
count time series. The techniques use a latent Gaussian process and a
distributional transformation to construct stationary series with very flexible
correlation features that can have any pre-specified marginal distribution,
including the classical Poisson, generalized Poisson, negative binomial, and
binomial count structures. A Gaussian pseudo-likelihood estimation paradigm,
based only on the mean and autocovariance function of the count series, is
developed via some new Hermite expansions. Particle filtering methods are
studied to approximate the true likelihood of the count series. Here,
connections to hidden Markov models and other copula likelihood approximations
are made. The efficacy of the approach is demonstrated and the methods are used
to analyze a count series containing the annual number of no-hitter baseball
games pitched in major league baseball since 1893
Integrable Floquet dynamics, generalized exclusion processes and "fused" matrix ansatz
We present a general method for constructing integrable stochastic processes,
with two-step discrete time Floquet dynamics, from the transfer matrix
formalism. The models can be interpreted as a discrete time parallel update.
The method can be applied for both periodic and open boundary conditions. We
also show how the stationary distribution can be built as a matrix product
state. As an illustration we construct a parallel discrete time dynamics
associated with the R-matrix of the SSEP and of the ASEP, and provide the
associated stationary distributions in a matrix product form. We use this
general framework to introduce new integrable generalized exclusion processes,
where a fixed number of particles is allowed on each lattice site in opposition
to the (single particle) exclusion process models. They are constructed using
the fusion procedure of R-matrices (and K-matrices for open boundary
conditions) for the SSEP and ASEP. We develop a new method, that we named
"fused" matrix ansatz, to build explicitly the stationary distribution in a
matrix product form. We use this algebraic structure to compute physical
observables such as the correlation functions and the mean particle current.Comment: 33 pages, to appear in Nuclear Physics
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