23,325 research outputs found
Capacity estimation of two-dimensional channels using Sequential Monte Carlo
We derive a new Sequential-Monte-Carlo-based algorithm to estimate the
capacity of two-dimensional channel models. The focus is on computing the
noiseless capacity of the 2-D one-infinity run-length limited constrained
channel, but the underlying idea is generally applicable. The proposed
algorithm is profiled against a state-of-the-art method, yielding more than an
order of magnitude improvement in estimation accuracy for a given computation
time
Spectral unmixing of Multispectral Lidar signals
In this paper, we present a Bayesian approach for spectral unmixing of
multispectral Lidar (MSL) data associated with surface reflection from targeted
surfaces composed of several known materials. The problem addressed is the
estimation of the positions and area distribution of each material. In the
Bayesian framework, appropriate prior distributions are assigned to the unknown
model parameters and a Markov chain Monte Carlo method is used to sample the
resulting posterior distribution. The performance of the proposed algorithm is
evaluated using synthetic MSL signals, for which single and multi-layered
models are derived. To evaluate the expected estimation performance associated
with MSL signal analysis, a Cramer-Rao lower bound associated with model
considered is also derived, and compared with the experimental data. Both the
theoretical lower bound and the experimental analysis will be of primary
assistance in future instrument design
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Many machine learning problems can be framed in the context of estimating
functions, and often these are time-dependent functions that are estimated in
real-time as observations arrive. Gaussian processes (GPs) are an attractive
choice for modeling real-valued nonlinear functions due to their flexibility
and uncertainty quantification. However, the typical GP regression model
suffers from several drawbacks: i) Conventional GP inference scales
with respect to the number of observations; ii) updating a GP model
sequentially is not trivial; and iii) covariance kernels often enforce
stationarity constraints on the function, while GPs with non-stationary
covariance kernels are often intractable to use in practice. To overcome these
issues, we propose an online sequential Monte Carlo algorithm to fit mixtures
of GPs that capture non-stationary behavior while allowing for fast,
distributed inference. By formulating hyperparameter optimization as a
multi-armed bandit problem, we accelerate mixing for real time inference. Our
approach empirically improves performance over state-of-the-art methods for
online GP estimation in the context of prediction for simulated non-stationary
data and hospital time series data
Adaptive Sampling Approach to the Negative Sign Problem in the Auxiliary Field Quantum Monte Carlo Method
We propose a new sampling method to calculate the ground state of interacting
quantum systems. This method, which we call the adaptive sampling quantum monte
carlo (ASQMC) method utilises information from the high temperature density
matrix derived from the monte carlo steps. With the ASQMC method, the negative
sign ratio is greatly reduced and it becomes zero in the limit
goes to zero even without imposing any constraint such like the constraint path
(CP) condition. Comparisons with numerical results obtained by using other
methods are made and we find the ASQMC method gives accurate results over wide
regions of physical parameters values.Comment: 8 pages, 7 figure
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