2015年考研英语阅读理解考前专项训练（56）
发布时间：2012/10/31 13:29:28 来源：城市网学院 编辑：admin
【英译汉】
Science， being a human activity， is not immune to fashion. （1）For example，one of the first.mathematicians to Bayes。who was born in l 702 and died in l 76 1.His ideas about the prediction of future events from one or two examples were popular for a while，and have never been fundamentally challenged.（2）But they were eventually overwhelmed by those of the“frequentist”school。which developed the methods based on sampling from a large population that now dominate the field and are used to predict things as diverse as the out#es of elections and preferences for chocolate bars.
Recently，however，Bayes's ideas have made a #eback among #puter scientists trying to design software with human-like intelligence.Bayesian reasoning now lies at the heart of leading internet search engines and automated“help wizards”。That has prompted some psychologists to ask if the human brain itself might be a Bayesian-reasoning machine.（3）They suggest that the Bayesian capacity to draw strong inferences from sparse data could be crucial to the way the mind perceives the world，plans actions， #prehends and learns language，reasons from correlation to causation，and even understands the goals and beliefs of other minds.
（4）These researchers have conducted laboratory experiments that convince them they are on the right track，but only recently have they begun to look at whether the brain copes with everyday j udgments in the real world in a Bayesian manner.In research to be published later this year in Psychological Science.Thomas Griffiths of Brown University in Rhode Island and Joshua Tenenbaum of the Massachusetts Institute of Technology put the idea of a Bayesian brain to a quotidian test.They found that it passes with flying eolours.
The key to successful Bayesian reasoning is not in having an extensive，unbiased sample，which is the eternal worry of frequentists。but rather in having an appropriate“prior”，as it is known to the cognoscenti.（5）This prior is an assumption about the way the world works—in essence，a hypothesis about reality that can be expressed as a mathematical probability distribution of the frequency with which events of a particular magnitude happen.
超纲词汇
cognoscente n.博学者，行家
#eback n.恢复，复原
quotidian adj.每日的，每日发作的，平凡的，日常的
sparse n由。稀少的，稀疏的
unbiased adj.没有偏见的
wizard n.向导，范例 [NextPage] 【参考译文】
（1）例如，一个名叫托马斯·贝叶斯的英国牧师是最早研究概率论的数学家之一。他1702年出生，1761年去世。
（2）但是，“频率论”学派的想法最终压倒了贝叶斯的想法。“频率论”学派研究出以在大群体中取样为基础的方法。 “频率论”学派的方法如今主宰了这个领域，被用来预测各种不同的事物，比如，选举结果，以及对不同巧克力块 的偏爱程度等。
（3）这些心理学家提出，贝叶斯从少量数据得出合理推论的能力可能与人脑的各种能力密切相关。人脑能认识世 界、安排活动、理解并学习语言、从相互关系到因果关系进行推理，甚至能理解他人的目标和信念。
（4）这些研究人员在实验室做了实验。这些实验使他们相信自己方向正确。但是，只不过最近，他们才开始考虑人 脑是否依照贝叶斯模式处理真实世界的日常问题。
（5）这个前提是对世界运行方式所做的假设，实质上是对真实事物的假设。这个假设是用大数量事件发生频率的 数学概率分布表达出来的。
【句结构解析】
（1）For example。one of the first mathematicians Eto study the subject of probability theory]was an English clergy man[called Thomas Bayes]，[who was born in l702 and died in l761].
（2）But they were eventually overwhelmed by those of the“frequentist”school，[which developed the methods （based on sampling from a large population）（that now dominate the field and are used to predict things<as di— verse as the out#es of elections and preferences for chocolate bars））].
（3）They suggest[that the Bayesian capacity（to draw strong inferences from sparse data）could be crucial to the way（the mind“perceives the world，pIans actions，”#prehends and learns language，“reasons from corre— lation to causation，and even understands the goals and beliefs of other minds）].
（4）These researchers have conducted laboratory experiments[-that convince them（they are oD the right track）]， but only recently have they begun to look at[whether the brain copes with everyday judgments in the real world in a Bayesian manner3.
（5）This prior is an assumption about the way[the world worksJ--in essence，a hypothesis about reality--[that can be expressed as a mathematical probability distribution of the frequency（with which events of a particular ma9— nitude happen）].
全文翻译
科学，作为人类的行为，不可避免地会追求时髦。（1）例如，一个名叫托马斯·贝叶斯的英国牧师是最早研究概率论的数学家之一。他1702年出生，l761年去世。他所提出的根据一到两个例子来预测未来事件的理论曾经风靡一时，并且也从未受到过根本性的挑战。（2）但是，”频率论“学派的想法最终压倒了贝叶斯的想法。”频率论“学派研究出以在大群体中取样为基础的方法。”频率论“学派的方法如今主宰了这个领域，被用来预测各种不同的事物，比如，选举结果，以及对不同巧克力块的偏爱程度等。
但是最近，在尝试设计具有人工智能的软件的计算机科学家们当中，贝叶斯的理念又有重燃的趋势。贝叶斯推想模式正是目前世界领先的互联网搜索引擎和自动化”帮助精灵“的核心所在。这也引起了一些心理学者的思考，人脑是否可能就是以贝叶斯式推理运作的机器。（3）这些心理学家提出，贝叶斯从少量数据得出合理推论的能力可能与人脑的各种能力密切相关。人脑能认识世界、安排活动、理解并学习语言、从相互关系到因果关系进行推理，甚至能理解他人的目标和信念。
（4）这些研究人员在实验室做了实验。这些实验使他们相信自己方向正确。但是，只不过最近，他们才开始考虑人脑是否依照贝叶斯模式处理真实世界的日常问题。在今年下半年将在《心理科学》上发表的一份研究中，罗德岛的布朗大学的托马斯·格里菲斯和麻省理工学院的约书亚·特南堡将一个贝叶斯式大脑模型置于一个日常性测试当中。他们发现其成功地通过了测试。
贝叶斯式推理的成功并不需要一个广泛而公平的样本，这样的样本只有频率论者们才会一直担心，而贝叶斯推理主要需要一个如同博学者所提倡的适当的”前提“。（5）这个前提是对世界运行方式所做的假设，实质上是对真实事物的假设。这个假设是用大数量事件发生频率的数学概率分布表达出来的。
