60,232 research outputs found
Noisy Tensor Completion for Tensors with a Sparse Canonical Polyadic Factor
In this paper we study the problem of noisy tensor completion for tensors
that admit a canonical polyadic or CANDECOMP/PARAFAC (CP) decomposition with
one of the factors being sparse. We present general theoretical error bounds
for an estimate obtained by using a complexity-regularized maximum likelihood
principle and then instantiate these bounds for the case of additive white
Gaussian noise. We also provide an ADMM-type algorithm for solving the
complexity-regularized maximum likelihood problem and validate the theoretical
finding via experiments on synthetic data set
Minimax and adaptive estimation of the Wigner function in quantum homodyne tomography with noisy data
We estimate the quantum state of a light beam from results of quantum
homodyne measurements performed on identically prepared quantum systems. The
state is represented through the Wigner function, a generalized probability
density on which may take negative values and must respect
intrinsic positivity constraints imposed by quantum physics. The effect of the
losses due to detection inefficiencies, which are always present in a real
experiment, is the addition to the tomographic data of independent Gaussian
noise. We construct a kernel estimator for the Wigner function, prove that it
is minimax efficient for the pointwise risk over a class of infinitely
differentiable functions, and implement it for numerical results. We construct
adaptive estimators, that is, which do not depend on the smoothness parameters,
and prove that in some setups they attain the minimax rates for the
corresponding smoothness class.Comment: Published at http://dx.doi.org/10.1214/009053606000001488 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Statistical Physics Analysis of Maximum a Posteriori Estimation for Multi-channel Hidden Markov Models
The performance of Maximum a posteriori (MAP) estimation is studied
analytically for binary symmetric multi-channel Hidden Markov processes. We
reduce the estimation problem to a 1D Ising spin model and define order
parameters that correspond to different characteristics of the MAP-estimated
sequence. The solution to the MAP estimation problem has different operational
regimes separated by first order phase transitions. The transition points for
-channel system with identical noise levels, are uniquely determined by
being odd or even, irrespective of the actual number of channels. We
demonstrate that for lower noise intensities, the number of solutions is
uniquely determined for odd , whereas for even there are exponentially
many solutions. We also develop a semi analytical approach to calculate the
estimation error without resorting to brute force simulations. Finally, we
examine the tradeoff between a system with single low-noise channel and one
with multiple noisy channels.Comment: The paper has been submitted to Journal of Statistical Physics with
submission number JOSS-S-12-0039
Measuring information-transfer delays
In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener’s principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics
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