How to estimate complex probability density?
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Estimating complex probability density functions (PDFs) can be achieved through various methods proposed in the literature. One approach involves utilizing nonparametric methods with neural networks (NNs) to obtain PDFs without imposing assumptions on the data generating process . Another method combines maximum entropy (MaxEnt) moments with Bayesian model selection to determine the most probable PDF model for a given dataset, especially for non-Gaussian distributions . Additionally, for stream mining tasks, an adaptive kernel density estimate (AKDE) has been developed to efficiently estimate PDFs over data streams, considering computational constraints . Furthermore, a hybrid method incorporating stochastic polynomial expansions, random variable transformation (RVT) techniques, and multidimensional integration schemes has been proposed to compute accurate approximations of PDFs for stochastic solutions in complex systems with uncertainties .
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| Papers (5) | Insight |
|---|---|
| Estimate complex probability density using a combined Maximum Entropy moments and Bayesian method, distinguishing between simple and complex models for sample sizes greater than 60. | |
26 Oct 2008 | The paper proposes an efficient and consistent nonparametric adaptive kernel density estimate (AKDE) framework using local regions and time-based sliding windows for estimating complex probability density structures in data streams. |
3 Citations | Estimate complex probability density using neural networks without assuming the data generating process, providing accurate PDF estimates for continuous and discrete distributions, including multivariate settings. |
| A hybrid method using stochastic polynomial expansions, the RVT technique, and multidimensional integration schemes can accurately estimate the probability density function of complex models with randomness. | |
2 Citations | Estimate complex probability density using neural networks without assuming the data generating process, providing accurate PDF estimates for continuous and discrete distributions, including multivariate settings. |
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