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- 实现在参数先验是高斯分布情况下，利用马尔科夫链蒙特卡洛算法来对Logistic 回归模型参数的后验分布进行抽样 -It implements different Markov Chain Monte Carlo strategies for sampling from the posterior distribution over the parameter values for Logistic Regression models with a Gaussian prior on th
SnOW
- This package provides a Maximum Entropy Modeling toolkit written in C++ with Python binding. It includes: Conditional Maximum Entropy Model L-BFGS Parameter Estimation GIS Parameter Estimation Gaussian Prior Smoothing C++ API
fbm.2004.11.10
- 《Software for Flexible Bayesian Modeling and Markov Chain Sampling》是机器学习领域专家Neal编写的用于Bayesian和马尔可夫链Linux下的C语言工具包。很有名，也很权威。 -This software supports Bayesian regression and classification models based on neural networks and Gaussian processes, and Ba
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- 在加入高斯白噪声的情况下,假设信噪比为10dB，形成传统波束形成法、Bartlett波束形成法、Capon波束形成法的空间谱图-Prior to joining the case of white Gaussian noise, assuming the SNR is 10dB, the formation of conventional beamforming method, Bartlett beamforming method, Capon Beamforming spectrum of
锂电池退化GPR
- 高斯过程回归是一种基于贝叶斯原理的统计机器学习方法，将先验分布通过贝叶斯定理转化成后验分布，与其他没有采用贝叶斯技巧的预测方法而言，高斯过程最大的优点是能方便地推断出超参数，同时也能方便地给出预测值的置信区间(Gaussian Process Regression is a statistical machine learning method based on Bayesian principle. It transforms prior distribution into posterio