4,697 research outputs found
Particle Gibbs with Ancestor Sampling
Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining
the two main tools used for Monte Carlo statistical inference: sequential Monte
Carlo (SMC) and Markov chain Monte Carlo (MCMC). We present a novel PMCMC
algorithm that we refer to as particle Gibbs with ancestor sampling (PGAS).
PGAS provides the data analyst with an off-the-shelf class of Markov kernels
that can be used to simulate the typically high-dimensional and highly
autocorrelated state trajectory in a state-space model. The ancestor sampling
procedure enables fast mixing of the PGAS kernel even when using seemingly few
particles in the underlying SMC sampler. This is important as it can
significantly reduce the computational burden that is typically associated with
using SMC. PGAS is conceptually similar to the existing PG with backward
simulation (PGBS) procedure. Instead of using separate forward and backward
sweeps as in PGBS, however, we achieve the same effect in a single forward
sweep. This makes PGAS well suited for addressing inference problems not only
in state-space models, but also in models with more complex dependencies, such
as non-Markovian, Bayesian nonparametric, and general probabilistic graphical
models
Automobile Accidents Associated With Cell Phone Use: Can Cell Phone Service Providers and Manufacturers Be Held Liable Under a Theory of Negligence?
Cell phone related car accidents have received a lot of attention in the press and academic journals over the past few years.1 Articles have discussed the impact of driving while using a hand-held or hands-free cell phone, and in some instances have identified liability on the part of employers. A number of cases have gone to the jury on employer liability based on respondeat superior, where the employer is held responsible for the actions of an employee acting within the scope of employment
Bayesian Nonparametric Inference of Switching Linear Dynamical Systems
Many complex dynamical phenomena can be effectively modeled by a system that
switches among a set of conditionally linear dynamical modes. We consider two
such models: the switching linear dynamical system (SLDS) and the switching
vector autoregressive (VAR) process. Our Bayesian nonparametric approach
utilizes a hierarchical Dirichlet process prior to learn an unknown number of
persistent, smooth dynamical modes. We additionally employ automatic relevance
determination to infer a sparse set of dynamic dependencies allowing us to
learn SLDS with varying state dimension or switching VAR processes with varying
autoregressive order. We develop a sampling algorithm that combines a truncated
approximation to the Dirichlet process with efficient joint sampling of the
mode and state sequences. The utility and flexibility of our model are
demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA
stock index, and a maneuvering target tracking application.Comment: 50 pages, 7 figure
A sticky HDP-HMM with application to speaker diarization
We consider the problem of speaker diarization, the problem of segmenting an
audio recording of a meeting into temporal segments corresponding to individual
speakers. The problem is rendered particularly difficult by the fact that we
are not allowed to assume knowledge of the number of people participating in
the meeting. To address this problem, we take a Bayesian nonparametric approach
to speaker diarization that builds on the hierarchical Dirichlet process hidden
Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006)
1566--1581]. Although the basic HDP-HMM tends to over-segment the audio
data---creating redundant states and rapidly switching among them---we describe
an augmented HDP-HMM that provides effective control over the switching rate.
We also show that this augmentation makes it possible to treat emission
distributions nonparametrically. To scale the resulting architecture to
realistic diarization problems, we develop a sampling algorithm that employs a
truncated approximation of the Dirichlet process to jointly resample the full
state sequence, greatly improving mixing rates. Working with a benchmark NIST
data set, we show that our Bayesian nonparametric architecture yields
state-of-the-art speaker diarization results.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS395 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Rigorous free fermion entanglement renormalization from wavelet theory
We construct entanglement renormalization schemes which provably approximate
the ground states of non-interacting fermion nearest-neighbor hopping
Hamiltonians on the one-dimensional discrete line and the two-dimensional
square lattice. These schemes give hierarchical quantum circuits which build up
the states from unentangled degrees of freedom. The circuits are based on pairs
of discrete wavelet transforms which are approximately related by a
"half-shift": translation by half a unit cell. The presence of the Fermi
surface in the two-dimensional model requires a special kind of circuit
architecture to properly capture the entanglement in the ground state. We show
how the error in the approximation can be controlled without ever performing a
variational optimization.Comment: 15 pages, 10 figures, one theore
Serum N-propeptide of collagen IIA (PIIANP) as a marker of radiographic osteoarthritis burden
Cartilage homeostasis relies on a balance of catabolism and anabolism of cartilage matrix. Our goal was to evaluate the burden of radiographic osteoarthritis and serum levels of type IIA procollagen amino terminal propeptide (sPIIANP), a biomarker representing type II collagen synthesis, in osteoarthritis
Stochastic exclusion processes versus coherent transport
Stochastic exclusion processes play an integral role in the physics of
non-equilibrium statistical mechanics. These models are Markovian processes,
described by a classical master equation. In this paper a quantum mechanical
version of a stochastic hopping process in one dimension is formulated in terms
of a quantum master equation. This allows the investigation of coherent and
stochastic evolution in the same formal framework. The focus lies on the
non-equilibrium steady state. Two stochastic model systems are considered, the
totally asymmetric exclusion process and the fully symmetric exclusion process.
The steady state transport properties of these models is compared to the case
with additional coherent evolution, generated by the -Hamiltonian
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