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- Bootstrap resampling. (1)
- Calculus of variations (1)
- Consensus Model (1)
- Differential geometry (1)
- Entropy (1)
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- Financial time series (1)
- Fourier Analysis (1)
- Functional analysis (1)
- Keywords: newsvendor models (1)
- Minimal path (1)
- Monte Carlo sampling (1)
- Mutual Information (1)
- Numerical approximation (1)
- Signal Processing (1)
- Singular Spectrum Analysis (1)
- Singular Value Decomposition (1)
- Takens Vectors (1)
- Tensors (1)
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Newsvendor Models With Monte Carlo Sampling, Ijeoma W. Ekwegh
Newsvendor Models With Monte Carlo Sampling, Ijeoma W. Ekwegh
Electronic Theses and Dissertations
Newsvendor Models with Monte Carlo Sampling by Ijeoma Winifred Ekwegh The newsvendor model is used in solving inventory problems in which demand is random. In this thesis, we will focus on a method of using Monte Carlo sampling to estimate the order quantity that will either maximizes revenue or minimizes cost given that demand is uncertain. Given data, the Monte Carlo approach will be used in sampling data over scenarios and also estimating the probability density function. A bootstrapping process yields an empirical distribution for the order quantity that will maximize the expected profit. Finally, this method will be used …
An Algorithm For The Machine Calculation Of Minimal Paths, Robert Whitinger
An Algorithm For The Machine Calculation Of Minimal Paths, Robert Whitinger
Electronic Theses and Dissertations
Problems involving the minimization of functionals date back to antiquity. The mathematics of the calculus of variations has provided a framework for the analytical solution of a limited class of such problems. This paper describes a numerical approximation technique for obtaining machine solutions to minimal path problems. It is shown that this technique is applicable not only to the common case of finding geodesics on parameterized surfaces in R3, but also to the general case of finding minimal functionals on hypersurfaces in Rn associated with an arbitrary metric.
Takens Theorem With Singular Spectrum Analysis Applied To Noisy Time Series, Thomas K. Torku
Takens Theorem With Singular Spectrum Analysis Applied To Noisy Time Series, Thomas K. Torku
Electronic Theses and Dissertations
The evolution of big data has led to financial time series becoming increasingly complex, noisy, non-stationary and nonlinear. Takens theorem can be used to analyze and forecast nonlinear time series, but even small amounts of noise can hopelessly corrupt a Takens approach. In contrast, Singular Spectrum Analysis is an excellent tool for both forecasting and noise reduction. Fortunately, it is possible to combine the Takens approach with Singular Spectrum analysis (SSA), and in fact, estimation of key parameters in Takens theorem is performed with Singular Spectrum Analysis. In this thesis, we combine the denoising abilities of SSA with the Takens …
Consensus Model Of Families Of Images Using Tensor-Based Fourier Analysis, Joel A. Shelton
Consensus Model Of Families Of Images Using Tensor-Based Fourier Analysis, Joel A. Shelton
Electronic Theses and Dissertations
A consensus model is a statistical approach that uses a family of signals or in our case, a family of images to generate a predictive model. In this thesis, we consider a family of images that are represented as tensors. In particular, our images are (2,0)-tensors. The consensus model is produced by utilizing the quantum Fourier transform of a family of images as tensors to transform images to images. We write a quantum Fourier transform in the numerical computation library for Python, known as Theano to produce the consensus spectrum. From the consensus spectrum, we produce the consensus model via …