"Serious researchers in combinatorics or algorithm design will wish to read the book in its entirety...the book may also be enjoyed on a lighter level since the different chapters are largely independent and so it is possible to pick out gems in one's own area..." -Formal Aspects of Computing This

A Probabilistic Theory of Pattern Recognition 完整 清晰版

Probabilistic graphical models is a technique in machine learning that uses the concepts of graph theory to concisely represent and optimally predict values in our data problems. Graphical models gives us techniques to find complex patterns in the data and are widely used in the field of speech recognition, information extraction, image segmentation, and modeling gene regulatory networks. This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also run different inference algorithms on them. There is an entire chapter that goes on to cover Naive Bayes model and Hidden Markov models. These models have been thoroughly discussed using real-world examples.

火炬 PyTorchTS是一个概率时间序列预测框架，通过利用作为其后端API以及用于加载，转换和回测时间序列数据集，提供了最新的PyTorch时间序列模型。 安装 $ pip3 install pytorchts 快速开始 在这里，我们通过GluonTS自述文件重点介绍了API的更改。 import matplotlib . pyplot as plt import pandas as pd import torch from gluonts . dataset . common import ListDataset from gluonts . dataset . util import to_pandas from pts . model . deepar import DeepAREstimator from pts import Trainer 这个简单的示例说明了如何在一些数

研究概率推理不错的一本书，提出贝叶斯网络的首本书

Send_Hardest_Problems_My_Way__Probabilistic_Path_Prioritization_for_Hybrid_Fuzzing_一种基于蒙特卡洛方法的混合式漏洞挖掘工具 安全架构 网络安全 信息安全研究 安全体系 业务风控

Probabilistic Robotics, by Sebastian THRUN Stanford.SLAM必读书籍

Classic book written by Michael I. Jordan & Christopher M. Bishop.

Probabilistic Graphical Models - Principles and Techniques.pdf高清版，可以下载下来，网上打印书籍哦

Probabilistic video stabilization using Kalman filtering and mosaicking