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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写贝叶斯统计方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写贝叶斯统计代写方面经验极为丰富，各种代写贝叶斯统计相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|贝叶斯统计代写Bayesian statistics代考|THE INDUCTION RULE

It is considered a truism that not all inferences are deductive. Arguments which compel assent, granted their premisses, are said to be limiting cases of the more general class of those whose premisses support their conclusions inconclusively. Such arguments are variously labelled ‘inductive’, ‘informative’, or ‘probable’. The conclusions of deductive arguments from true premisses are invariably true, while conclusions of probable arguments from true premisses are said to be ‘for the most part true’.. One speaks also of ‘arguments by analogy’, ‘statistical syllogisms’, and the like – terms which further argue that induction and deduction are but two sides of the same inferential coin.

Whatever the rubric preferred, non-deductive inferences have notoriously eluded precise characterisation. In particular, no clear-cut analogue of the semantical concept of validity has emerged for inductive arguments. This, despite heeroic attempts of Carnap, Hempel and others to extend the deductive concept of implication to an inductive concept of partial implication or confirmation. $^2$

Quite distinguishable from the work of the Carnapians, an older tradition persists which grounds all non-demonstrative inference on a single principle, the induction rule. Classic formulations of it assert that events conjoined in the past are likely to be conjoined in the future, that like causes have like effects, or that generalizations are the more probable the greater the number of their attested cases. Under the first of these formulations, the principle constitutes an organon of discovery, but it has also been thought to provide a rule of estimation (Reichenbach’s ‘straight rule’), a measure of confirmation (as in the third formulation), and a method of computing predictive probabilities. Only the foundation upon which it rests has been regarded as obscure and standing in need of elucidation. This is the celebrated problem of justifying induction. Otherwise put, the problem is to explain why ‘inductive methods’ work. But it is not clear just what has worked, historically or comparatively speaking.

The corpus of inductive methods can be pinned down somewhat in terms of the induction rule. But that leaves one no place to go so far as justification is concerned. For if the rule is presupposed in the extrapolation of its own successful performance, then we are back where we began – in the Humean bind. It was perhaps inevitable that someone should eventually make a merit of this ‘scandal’, and declare the very impossibility of justifying or repudiating the rule without pre-supposing it as constituting, if not justification, then ‘vindication’. 3 Related attempts to unravel Hume’s knot have argued justification is uncalled for, that “it is an analytic proposition… that … the evidence for a generalization is strong in proportion as the number of favorable instances … is great”. ${ }^4$ To ask whether it is reasonable to place reliance on the induction rule, the attempted dissolution continues, “is like asking whether it is reasonable to proportion the degree of one’s convictions to the strength of the evidence”.

## 统计代写|贝叶斯统计代写Bayesian statistics代考|THE IMPORT OF DATA

Classical formulations omit reference to background knowledge and so pose a spurious optimization problem: there is no optimal inductive method that depends only on frequency counts. For frequency counts have no import whatever in abstraction from a probability model. Broadly speaking, inductive reasoning allows us to base conclusions about the future behavior of a process on its past behavior. But the import of data about the past for the future is a function of the laws in accordance with which the process develops.

Polya urn models nicely illustrate this point. Imagine an urn containing $b$ black and $r$ red balls. A ball is drawn at random and replaced. But in addition, $c$ balls of the color drawn and $d$ balls of the opposite color are placed in the urn. A new drawing is made from the urn, now containing $b+r+c+d$ balls, and the procedure repeated. ${ }^6$ In Polya’s original scheme, $d=0$ and $c>0$, so that the drawing of either color increases the probability that the same color will be drawn next. This gives a rough model of contagion, where the quotient $c /(b+r)$ measures the rate of contagion. Other bounds on the random sampling without replacement, terminating after $b+r$ steps.

Now it should be clear that a given relative frequency of black balls on past drawings from a Polya urn can connote either an increase or a decrease in the probability of drawing a black ball on the ensuing trial, depending on the specifications of the parameters $b, r, c, d$. Nevertheless, by the lights of the induction rule, an abstract pattern of trial outcomes, a binary sequence, would have the same import for the future development of a process evincing fatigue as it would for a contagious process. For the trial outcomes are all that the induction rule takes explicitly into account. But in a learning experiment, for example, the probability of a correct response will usually increase with each correct response, while the probability of an incorrect response will decrease with each incorrect response, particularly if responses are corrected or reinforced. We are not merely iterating the truism that probabilities are relative to data. Rather we are urging that the data themselves depend for their import on the posited model of the experiment, the theoretical lens through which the outcomes are viewed and interpreted.

# 假设检验代写

## 统计代写|贝叶斯统计代写Bayesian statistics代考|THE IMPORT OF DATA

Polya 骨灰盒模型很好地说明了这一点。想象一个包含b黑色和r红球。随机抽取一个球并替换。但除此之外，C绘制的颜色的球和d相反颜色的球被放置在骨灰盒中。用瓮制作了一张新画，现在包含b+r+C+d球，并重复该过程。6在波利亚最初的计划中，d=0和C>0, 使得绘制任何一种颜色都会增加下一次绘制相同颜色的概率。这给出了一个粗略的传染模型，其中商C/(b+r)衡量传染率。无放回随机抽样的其他界限，终止于b+r脚步。

## 有限元方法代写

assignmentutor™作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师