Search results for "Statistical manifold"

showing 2 items of 2 documents

Information geometry of Gaussian channels

2009

We define a local Riemannian metric tensor in the manifold of Gaussian channels and the distance that it induces. We adopt an information-geometric approach and define a metric derived from the Bures-Fisher metric for quantum states. The resulting metric inherits several desirable properties from the Bures-Fisher metric and is operationally motivated from distinguishability considerations: It serves as an upper bound to the attainable quantum Fisher information for the channel parameters using Gaussian states, under generic constraints on the physically available resources. Our approach naturally includes the use of entangled Gaussian probe states. We prove that the metric enjoys some desir…

PhysicsQuantum PhysicsGaussianFOS: Physical sciencesMathematical Physics (math-ph)01 natural sciencesAtomic and Molecular Physics and Optics010305 fluids & plasmasStatistical manifoldIntrinsic metricCondensed Matter - Other Condensed Mattersymbols.namesakeQuantum mechanics0103 physical sciencesMetric (mathematics)symbolsApplied mathematicsInformation geometryFidelity of quantum statesQuantum Physics (quant-ph)010306 general physicsQuantum information scienceFisher information metricMathematical PhysicsOther Condensed Matter (cond-mat.other)
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Approximation of functions over manifolds : A Moving Least-Squares approach

2021

We present an algorithm for approximating a function defined over a $d$-dimensional manifold utilizing only noisy function values at locations sampled from the manifold with noise. To produce the approximation we do not require any knowledge regarding the manifold other than its dimension $d$. We use the Manifold Moving Least-Squares approach of (Sober and Levin 2016) to reconstruct the atlas of charts and the approximation is built on-top of those charts. The resulting approximant is shown to be a function defined over a neighborhood of a manifold, approximating the originally sampled manifold. In other words, given a new point, located near the manifold, the approximation can be evaluated…

Computational Geometry (cs.CG)FOS: Computer and information sciencesComputer Science - Machine LearningClosed manifolddimension reductionMachine Learning (stat.ML)010103 numerical & computational mathematicsComplex dimensionTopology01 natural sciencesMachine Learning (cs.LG)Volume formComputer Science - GraphicsStatistics - Machine Learningmanifold learningApplied mathematics0101 mathematicsfunktiotMathematicsManifold alignmentAtlas (topology)Applied Mathematicshigh dimensional approximationManifoldGraphics (cs.GR)Statistical manifold010101 applied mathematicsregression over manifoldsComputational Mathematicsout-of-sample extensionComputer Science - Computational Geometrynumeerinen analyysimonistotapproksimointimoving least-squaresCenter manifold
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