Bayesian Networks is a model that efficiently represents knowledge and probabilistic inference and is a popular graphics decision-making analysis tool.
贝叶斯网络是在不确定性环境下有效的知识表示方式和概率推理模型，是一种流行的图形决策化分析工具。
“What’s surprising in our study is that action games improved probabilistic inference not just for the act of gaming, but for unrelated and rather dull tasks,” Bavelier says.
巴韦利埃表示：“令人感到吃惊的是，靠玩动作游戏提高的概率推理能力并不仅限于游戏，同时也可用于完成与游戏无关的更为鼓噪的任务。”
What is known is that people make decisions based on probabilities that are constantly being calculated and refined in their heads-something called "probabilistic inference".
已知的情况是，人们基于其头脑中不断计算和细化的概率(即所谓“概率干预”)来作出决策。
What is known is that people make decisions based on probabilities that are constantly being calculated and refined in their heads—something called "probabilistic inference".
而已知的是，人们是通过在脑中不断地计算和修正概率来做出决定的，这被叫做“概率推理”。
What is known, however, is that people make decisions based on probabilities, which are constantly being calculated and refined in their heads—something called “probabilistic inference”.
然而，我们知道的是，人们做出的决定基于对概率的判断，而此他们在头脑中不断计算和精确这个判断——这叫做“概率推理”。
Those who get a lot of practice, say, killing zombies attacking from haphazard directions in a shifting, postapocalyptic landscape pump up their probabilistic inference powers, Bavelier proposes.
巴韦利埃指出，在诡异的后启示录世界消灭从任意方向展开攻击的敌人能够提高经验丰富的游戏玩家的概率推理能力。
OBJECTIVE: To introduce the mental model theory and probabilistic theory with their disputation and tradeoff in conditional inference domain.
目的：介绍条件推理领域心理模型理论与概率理论以及它们之间的争论与融合。
Emphasizes history of probability theory and computational approaches to probabilistic and causal inference.
重点介绍机率理论的历史，以及机率理论和因果推理的计算方法。
Bayesian learning Theory represents uncertainty with probability and learning and inference are realized by probabilistic rules.
贝叶斯学习理论使用概率去表示所有形式的不确定性，通过概率规则来实现学习和推理过程。
Emphasizes history of probability theory and computational approaches to probabilistic and causal inference.
重点介绍概率理论的历史，以及盖然论和因果推理的计算方法。
Emphasizes history of probability theory and computational approaches to probabilistic and causal inference.
重点介绍概率理论的历史，以及盖然论和因果推理的计算方法。
应用推荐