'Expectation values' diaporamas de présentation

Statistics

Statistics. We collect a sample of data, what do we do with it? Estimate parameters (possibly of some model) Test whether a particular theory is consistent with our data (hypothesis testing) Statistics is a set of tools that allows us to achieve these goals. Statistics. Preliminaries.

1.03k views • 82 slides

856 views • 72 slides

Kondo, Fano and Dicke effects in side quantum dots

943 views • 60 slides

Introduction to Tensor Network States

Introduction to Tensor Network States. Sukhwinder Singh Macquarie University (Sydney). Contents. The quantum many body problem. Diagrammatic Notation What is a tensor network? Example 1 : MPS Example 2 : MERA. Quantum many body system in 1-D.

1.39k views • 58 slides

Chapter 2

Chapter 2. The Schrodinger Equation. wave function of a free particle. Time dependent Schrodinger equation. The probability density. Expectation values for momentum and energy. Operators. Relation between expectation values and operators. Wave equation. Dr. Maha Torjman.

796 views • 47 slides

992 views • 42 slides

Statistical Postprocessing of Weather Parameters for a High-Resolution Limited-Area Model

Statistical Postprocessing of Weather Parameters for a High-Resolution Limited-Area Model. Susanne Theis Andreas Hense. Ulrich Damrath Volker Renner. Overview. - Introduction - Description of Method - Examples - Verification Results - Calibration of Reliability Diagrams

528 views • 41 slides

1.47k views • 40 slides

657 views • 35 slides

PEPS, matrix product operators and the algebraic Bethe ansatz

PEPS, matrix product operators and the algebraic Bethe ansatz. Frank Verstraete University of Vienna Valentin Murg , Ignacio Cirac (MPQ) B. Pirvu (Vienna). Matrix Product States and Projected Entangled Pair States as variational states for simulating strongly correlated quantum systems.

454 views • 29 slides

Localization tensor calculations on quantum dots in DFT and VMC

476 views • 28 slides

II .2 Statistical Inference: Sampling and Estimation

II .2 Statistical Inference: Sampling and Estimation. A statistical model ? is a set of distributions (or regression functions ), e.g., all uni -modal, smooth distributions.

358 views • 25 slides

Time-independent Schrodinger eqn QM Ch.2, Physical Systems, 12.Jan.2003 EJZ

Time-independent Schrodinger eqn QM Ch.2, Physical Systems, 12.Jan.2003 EJZ. Assume the potential V(x) does not change in time. Use * separation of variables and * boundary conditions to solve for y . Once you know y , you can find any expectation value!. Outline:.

1.24k views • 20 slides

The effect of turbulence upon supernova neutrinos

The effect of turbulence upon supernova neutrinos. Jim Kneller NC State University NOW 2010. The neutrinos we will detect from the next SN in our Galaxy will tell us much about the explosion and the neutrino.

286 views • 19 slides

Coordinate TransformationS :

294 views • 18 slides

Statistical Data Analysis: Lecture 2

399 views • 17 slides

Statistical modelling and latent variables.

Statistical modelling and latent variables. . Constructing models based on insight and motivation. Statistical modelling - why?. A statistical model describes a possible distribution of incoming data, given some (unknown) parameter values - likelihood.

272 views • 15 slides

443 views • 15 slides

Checking the Consistency of Local Density Matrices

Checking the Consistency of Local Density Matrices. Yi-Kai Liu Computer Science and Engineering University of California, San Diego y9liu@cs.ucsd.edu. The Consistency Problem. Consider an n-qubit system

280 views • 14 slides

Checking the Consistency of Local Density Matrices

Checking the Consistency of Local Density Matrices. Yi-Kai Liu Computer Science and Engineering University of California, San Diego y9liu@cs.ucsd.edu. The Consistency Problem. Consider an n-qubit system

243 views • 14 slides