The rise of probability theory changed that. Statistical inference compels us instead to rely on Fortuna as a servant of Minerva, to use chance and uncertainty to discover reliable knowledge. All flavors of statistical inference have this motivation. But Bayesian data analysis embraces it most fully, by using the language of chance to describe the plausibility of different possibilities. There are many ways to use the term “Bayesian.” But mainly it denotes a particular interpretation of probability. In modest terms, Bayesian inference is no more than counting the numbers of ways things can happen, according to our assumptions. Things that can happen more ways are more plausible. And since probability theory is just a calculus for counting, this means that we can use probability theory as a general way to represent plausibility, whether in reference to countable events in the world or rather theoretical constructs like parameters. Once you accept this gambit, the rest follows logically. Once we have defined our assumptions, Bayesian inference forces a purely logical way of processing that information to produce inference.

SystemVue仿真实例，配合SystemVue软件使用，推荐！

For Bayesian learning. For beginners. Easy but useful

详细描述40000 chips一例中每个主要函数及例子中语句的功能，便于QT初学者迅速看懂该例并学会如何开发该类系统。

Q3D 的例子，共5个，全英文，实用，适合初学者

微软最新深度相机在Unity中的实例，官方SDK中没有Azure Kinect Examples for Unity的实例。

FPGA Prototyping by VHDL Examples

MATLAB有限元工具箱的算例，适用于大家学习参考

贝叶斯网络的R语言实现，贝叶斯网络的R语言实现，贝叶斯网络的R语言实现，贝叶斯网络的R语言实现，贝叶斯网络的R语言实现。

PyQt官网开发的Demo，非常值得学习。直接使用IDE打开就可以了，启动文件是qtdemo文件下的qtdemo.py