Examples of using Bayesian inference in English and their translations into Chinese
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Bayesian Inference.
And what it means is we really are Bayesian inference machines.
这说明我们实际上都是天生的贝叶斯推断器.In the context of Bayesian inference, this is called the prior distribution P_X.
在贝叶斯推断的框架下，我们称之为先验分布P(X)。To implement MCMC in Python, we will use the PyMC3 Bayesian inference library.
要在Python中实现MCMC，我们需要使用PyMC3贝叶斯推理库。Bayesian inference works identically: we update our beliefs about an outcome;
贝叶斯推断的思想是完全一致的：我们更新自己对结果的信念；.One of my favorites is the interpretation of the methods as part of performing Bayesian inference.
我最喜欢的一个是将该方法阐释为贝叶斯推理执行的一部分。Bayesian inference is an important technique in statistics, and especially in mathematical statistics.
贝叶斯推断是统计学（特别是数理统计学）中很重要的技巧之一。Humans, it turns out, are not very good at Bayesian inference, at least when verbal reasoning is involved.
事实上，人类并不是很擅长贝叶斯推理，至少在牵涉到语言推理时是这样的。Bayesian inference considers both the strength of new evidence and the strength of your existing hypotheses.
贝叶斯推理同时考虑新证据的力量和已有假设的力量。We can illustrate it with an example from medical diagnosis,one of the“killer apps” of Bayesian inference.
我们可以用医学诊断中的一个例子来阐明，也就是贝叶斯推理的“杀手级应用”之一。Bayesian inference is a way to capture this in math so that we can make more accurate predictions.
而贝叶斯推理是此方式的数学实现形式，得益于此，我们可以做出更加精确的预测。贝叶斯分析方法有很多名称，“贝叶斯推理”、“逆概率”、“贝叶斯更新”….It is worth taking time to study thisfigure in detail as it illustrates several important aspects of Bayesian inference.
花时间仔细研究一下这幅图是很值得的，因为它说明了贝叶斯推断的一些重要的概念。NET is a framework for running Bayesian inference in graphical models that can also be used for probabilistic programming.
NET是一个在图形模型中运行贝叶斯推理的框架，它也可以用于概率编程。If the brain behaves as a Bayesian brain, we need to further ourunderstanding of how the brain actually implements Bayesian Inference.
如果大脑的确是按照贝叶斯脑假说的原理活动，那么我们需要进一步了解大脑是如何实现贝叶斯推理的。Several times I tried to learn MCMC and Bayesian inference, but every time I started reading the books, I soon gave up.
有几次我尝试着学习MCMC和贝叶斯推理，我每次从阅读书籍开始，结果却很快放弃。Bayesian inference has proved to be effective in machine learning- for example, to teach spam filters to recognize junk e-mail.
已经证明，贝叶斯推断能够有效地进行机器学习，例如，训练垃圾邮件过滤器认识垃圾邮件。And it's a monster,harnessing three of the most powerful ideas in science today: Bayesian inference, open-source software, and reproducible research.
它是一个怪物，今天治理三个最有力的思想在科学︰贝叶斯推理、开放源码软件和重复性研究。This chapter introduces Bayesian inference and Bayesian networks and shows how you can use these techniques in games.
本章要介绍贝叶斯推论和贝叶斯网络，教你怎么把这些技术应用在游戏中。In essence, every time that you do some form of numerical optimization,you're performing some Bayesian inference with particular assumptions and priors.
实质上，每当你做某种形式的数值优化时，你都会用特定的假设和先验来执行一些贝叶斯推理。Bayesian inference and evidence accumulation, which are cornerstone computations for AI(2), are basic unconscious mechanisms for humans.
贝叶斯推理和证据累积，是人工智能（2）的基础计算，也是人类基本的无意识机制。However, often difficult to deduce which part of the data is noise(cf. model selection, test set,minimum description length, Bayesian inference, etc.).
然而，常常很难判断那一部分数据是噪音（参见模型选择、测试集、最小描述长度、贝叶斯推断等等）。Here, we show how to use probabilistic programming and Bayesian inference to easily build tools that make better predictions for more effective decision making.
在此，我们展示如何使用概率编程和贝叶斯推理来轻松构建工具，更好地预测更有效的决策。Another initiative I like is The Automatic Statistician project,which searches models to discover the best explanation for your data using Bayesian inference.
另一个我很喜欢的项目是TheAutomaticStatistician，它能够使用贝叶斯推论找到能最好解释数据的模型。Stan is a specialized program that performs Bayesian inference, which is an approach, based on probability theory, for combining information from multiple sources.
斯坦是一个专门的程序，执行贝叶斯推理，是一种基于概率理论，合并来自多个源的信息的方法。It is, however, often difficult to deduce which part of the data is noise(cf. model selection, test set,minimum description length, Bayesian inference, etc.).
然而，常常很难判断那一部分数据是噪音（参见模型选择（英语：modelselection）、测试集（英语：testset）、最小描述长度、贝叶斯推断等等）。While"Bayesian inference" is sometimes held to include the approach to inference leading to optimal decisions, a more restricted view is taken here for simplicity.
虽然“贝叶斯推断”有时被认为包含了最优决策的推断方法，但为简单起见，这里采用更受限的观点。We intend to apply approximate Bayesian inference(ABC) to(possibly spatially heterogeneous) Susceptible-Exposed- Infectious-Removed(SEIR) stochastic epidemic models.
我们打算将近似贝叶斯推断(ABC)应用于（可能在空间上存在异质性）“易感-暴露-传染-移出”(SEIR)随机疫情模型。Bayesian inference is typically based on the posterior distribution. Many Bayesian point estimators are the posterior distribution's statistics of central tendency, e.g., its mean, median, or mode:.
贝叶斯推断通常基于后验分布。许多贝叶斯估计量是后验分布的集中趋势统计量，例如，它的均值，中位数或模式：.
