Simplified Computation for Non-Parametric Windows Method of Probability Density Function Estimation.
Recently Kadir et al. have proposed a method for estimating probability density functions (PDF) for digital signals which they call the Non-Parametric (NP) Windows method. The method involves constructing a continuous space representation of the discrete space and sampled signal using a suitable int...
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| Format: | Journal article |
| Language: | English |
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2011
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| _version_ | 1797092997907611648 |
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| author | Joshi, N Kadir, T Brady, S |
| author_facet | Joshi, N Kadir, T Brady, S |
| author_sort | Joshi, N |
| collection | OXFORD |
| description | Recently Kadir et al. have proposed a method for estimating probability density functions (PDF) for digital signals which they call the Non-Parametric (NP) Windows method. The method involves constructing a continuous space representation of the discrete space and sampled signal using a suitable interpolation method. NP Windows requires only a small number of observed signal samples to estimate the PDF and is completely data driven. In this short paper, we first develop analytical formulae to obtain the NP Windows PDF estimates for 1D, 2D, and 3D signals, for different interpolation methods. We then show that the original procedure to calculate the PDF estimate can be significantly simplified and made computationally more efficient by a judicious choice of the frame of reference. We have also outlined specific algorithmic details of the procedures enabling quick implementation. Our reformulation of the original concept has directly demonstrated a close link between the NP Windows method and the Kernel Density Estimator. |
| first_indexed | 2024-03-07T03:54:01Z |
| format | Journal article |
| id | oxford-uuid:c23dfa44-ba22-4847-b3d9-6e1b788418cb |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:54:01Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oxford-uuid:c23dfa44-ba22-4847-b3d9-6e1b788418cb2022-03-27T06:07:38ZSimplified Computation for Non-Parametric Windows Method of Probability Density Function Estimation.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c23dfa44-ba22-4847-b3d9-6e1b788418cbEnglishSymplectic Elements at Oxford2011Joshi, NKadir, TBrady, SRecently Kadir et al. have proposed a method for estimating probability density functions (PDF) for digital signals which they call the Non-Parametric (NP) Windows method. The method involves constructing a continuous space representation of the discrete space and sampled signal using a suitable interpolation method. NP Windows requires only a small number of observed signal samples to estimate the PDF and is completely data driven. In this short paper, we first develop analytical formulae to obtain the NP Windows PDF estimates for 1D, 2D, and 3D signals, for different interpolation methods. We then show that the original procedure to calculate the PDF estimate can be significantly simplified and made computationally more efficient by a judicious choice of the frame of reference. We have also outlined specific algorithmic details of the procedures enabling quick implementation. Our reformulation of the original concept has directly demonstrated a close link between the NP Windows method and the Kernel Density Estimator. |
| spellingShingle | Joshi, N Kadir, T Brady, S Simplified Computation for Non-Parametric Windows Method of Probability Density Function Estimation. |
| title | Simplified Computation for Non-Parametric Windows Method of Probability Density Function Estimation. |
| title_full | Simplified Computation for Non-Parametric Windows Method of Probability Density Function Estimation. |
| title_fullStr | Simplified Computation for Non-Parametric Windows Method of Probability Density Function Estimation. |
| title_full_unstemmed | Simplified Computation for Non-Parametric Windows Method of Probability Density Function Estimation. |
| title_short | Simplified Computation for Non-Parametric Windows Method of Probability Density Function Estimation. |
| title_sort | simplified computation for non parametric windows method of probability density function estimation |
| work_keys_str_mv | AT joshin simplifiedcomputationfornonparametricwindowsmethodofprobabilitydensityfunctionestimation AT kadirt simplifiedcomputationfornonparametricwindowsmethodofprobabilitydensityfunctionestimation AT bradys simplifiedcomputationfornonparametricwindowsmethodofprobabilitydensityfunctionestimation |