Conditional optimization of the functional computational kernel algorithm for approximating the probability density on the basis of a given sample
DOI10.1134/S0965542521090062zbMath1487.65007OpenAlexW3205181864MaRDI QIDQ2245905
T. E. Bulgakova, A. V. Voitishek
Publication date: 15 November 2021
Published in: Computational Mathematics and Mathematical Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1134/s0965542521090062
conditionally optimal parametersfunctional computational kernel algorithmfunctional computational statistical algorithmnumerical approximation of functionsnumerical functional approximation of probability density
Density estimation (62G07) Probabilistic models, generic numerical methods in probability and statistics (65C20) Numerical methods for integral equations (65R20) Fredholm integral equations (45B05) Distribution theory (60E99)
Cites Work
- Unnamed Item
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- Unnamed Item
- Unnamed Item
- Extremes and related properties of random sequences and processes
- Error estimation and optimization for the frequency polygon method in the \(\mathbb{C}\)-metric
- New Monte Carlo methods with estimating derivatives
- Convergence of discrete-stochastic numerical procedures with independent or weakly dependent estimators at grid nodes
- Optimization and applications. 10th international conference, OPTIMA 2019, Petrovac, Montenegro, September 30 -- October 4, 2019. Revised selected papers
- Improvement of multidimensional randomized Monte Carlo algorithms with ``splitting
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