A Bagging and Boosting Based Convexly Combined Optimum Mixture
Probabilistic Model
- URL: http://arxiv.org/abs/2106.05840v1
- Date: Tue, 8 Jun 2021 04:20:00 GMT
- Title: A Bagging and Boosting Based Convexly Combined Optimum Mixture
Probabilistic Model
- Authors: Mian Arif Shams Adnan, H. M. Miraz Mahmud
- Abstract summary: A bagging and boosting based convexly combined mixture probabilistic model has been suggested.
This model is a result of iteratively searching for obtaining the optimum probabilistic model that provides the maximum p value.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Unlike previous studies on mixture distributions, a bagging and boosting
based convexly combined mixture probabilistic model has been suggested. This
model is a result of iteratively searching for obtaining the optimum
probabilistic model that provides the maximum p value.
Related papers
- Mixup Regularization: A Probabilistic Perspective [11.501663622998697]
We introduce a novel framework for mixup regularization based on probabilistic fusion.
For data distributed according to a member of the exponential family, we show that likelihood functions can be analytically fused.
We propose an extension of probabilistic mixup, which allows for fusion of inputs at an arbitrary intermediate layer of the neural network.
arXiv Detail & Related papers (2025-02-19T15:39:14Z) - Generative Modeling with Bayesian Sample Inference [50.07758840675341]
We derive a novel generative model from the simple act of Gaussian posterior inference.
Treating the generated sample as an unknown variable to infer lets us formulate the sampling process in the language of Bayesian probability.
Our model uses a sequence of prediction and posterior update steps to narrow down the unknown sample from a broad initial belief.
arXiv Detail & Related papers (2025-02-11T14:27:10Z) - Be More Diverse than the Most Diverse: Online Selection of Diverse Mixtures of Generative Models [33.04472814852163]
In this work, we explore the selection of a mixture of multiple generative models.
We propose an online learning approach called Mixture Upper Confidence Bound (Mixture-UCB)
arXiv Detail & Related papers (2024-12-23T14:48:17Z) - Supervised Score-Based Modeling by Gradient Boosting [49.556736252628745]
We propose a Supervised Score-based Model (SSM) which can be viewed as a gradient boosting algorithm combining score matching.
We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy.
Our model outperforms existing models in both accuracy and inference time.
arXiv Detail & Related papers (2024-11-02T07:06:53Z) - Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis [0.0]
In many cases, the models used in the mixing process are similar.
The existence of such similar, or even redundant, models during the multimodeling process can result in misinterpretation of results and deterioration of predictive performance.
We show that by adding modelization to the proposed Bayesian Model Combination framework, one can arrive at better prediction accuracy and reach excellent uncertainty quantification performance.
arXiv Detail & Related papers (2024-05-17T15:01:29Z) - Local Bayesian Dirichlet mixing of imperfect models [0.0]
We study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses.
We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification.
arXiv Detail & Related papers (2023-11-02T21:02:40Z) - Differentiating Metropolis-Hastings to Optimize Intractable Densities [51.16801956665228]
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers.
We apply gradient-based optimization to objectives expressed as expectations over intractable target densities.
arXiv Detail & Related papers (2023-06-13T17:56:02Z) - Finite mixture of skewed sub-Gaussian stable distributions [0.0]
The proposed model contains the finite mixture of normal and skewed normal distributions.
It can be used as a powerful model for robust model-based clustering.
arXiv Detail & Related papers (2022-05-27T15:51:41Z) - BRIO: Bringing Order to Abstractive Summarization [107.97378285293507]
We propose a novel training paradigm which assumes a non-deterministic distribution.
Our method achieves a new state-of-the-art result on the CNN/DailyMail (47.78 ROUGE-1) and XSum (49.07 ROUGE-1) datasets.
arXiv Detail & Related papers (2022-03-31T05:19:38Z) - PSD Representations for Effective Probability Models [117.35298398434628]
We show that a recently proposed class of positive semi-definite (PSD) models for non-negative functions is particularly suited to this end.
We characterize both approximation and generalization capabilities of PSD models, showing that they enjoy strong theoretical guarantees.
Our results open the way to applications of PSD models to density estimation, decision theory and inference.
arXiv Detail & Related papers (2021-06-30T15:13:39Z) - Efficient Ensemble Model Generation for Uncertainty Estimation with
Bayesian Approximation in Segmentation [74.06904875527556]
We propose a generic and efficient segmentation framework to construct ensemble segmentation models.
In the proposed method, ensemble models can be efficiently generated by using the layer selection method.
We also devise a new pixel-wise uncertainty loss, which improves the predictive performance.
arXiv Detail & Related papers (2020-05-21T16:08:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.