社区
下载资源悬赏专区
帖子详情
Bayesian Methods, A General Introduction下载
weixin_39820835
2019-06-25 10:00:20
一篇关于贝叶斯的介绍性文章,96年的,需要的可以看看
相关下载链接:
//download.csdn.net/download/mynamehrm/2661114?utm_source=bbsseo
...全文
9
回复
打赏
收藏
Bayesian Methods, A General Introduction下载
一篇关于贝叶斯的介绍性文章,96年的,需要的可以看看 相关下载链接://download.csdn.net/download/mynamehrm/2661114?utm_source=bbsseo
复制链接
扫一扫
分享
转发到动态
举报
AI
作业
写回复
配置赞助广告
用AI写文章
回复
切换为时间正序
请发表友善的回复…
发表回复
打赏红包
Bayesian
Methods
, A
General
Int
roduct
ion
一篇关于贝叶斯的介绍性文章,96年的,需要的可以看看
bayesian
econometric
Bayesian
methods
are increasingly becoming attractive to researchers in many fields. Econometrics, however, is a field in which
Bayesian
methods
have had relatively less influence. A key reason for this absence is the lack of a suitable advanced undergraduate or graduate level textbook. Existing
Bayesian
books are either out-dated, and hence do not cover the computat
ion
al advances that have revolut
ion
ized the field of
Bayesian
econometrics since the late 1980s, or do not provide the broad coverage necessary for the student
int
erested in empirical work applying
Bayesian
methods
. For instance, Arnold Zellner’s seminal
Bayesian
econometrics book (Zellner, 1971) was published in 1971. Dale Poirier’s influential book (Poirier, 1995) focuses on the methodology and statistical theory underlying
Bayesian
and frequentist
methods
, but does not discuss models used by applied economists beyond regress
ion
. Other important
Bayesian
books, such as Bauwens, Lubrano and Richard (1999), deal only with particular areas of econometrics (e.g. time series models). In writing this book, my aim has been to fill the gap in the existing set of
Bayesian
textbooks, and create a
Bayesian
counterpart to the many popular non-
Bayesian
econometric textbooks now available (e.g. Greene, 1995). That is, my aim has been to write a book that covers a wide range of models and prepares the student to undertake applied work using
Bayesian
methods
. This book is
int
ended to be accessible to students with no prior training in econometrics, and only a single course in mathematics (e.g. basic calculus). Students will find a previous undergraduate course in probability and statistics useful; however Appendix B offers a brief
int
roduct
ion
to these topics for those without the prerequisite background. Throughout the book, I have tried to keep the level of mathematical sophisticat
ion
reasonably low. In contrast to other
Bayesian
and comparable frequentist textbooks, I have included more computer-related material. Modern
Bayesian
econometrics relies heavily on the computer, and developing some basic programming skills is essential for the applied
Bayesian
. The required level of computer programming skills is not that high, but I expect that this aspect of
Bayesian
econometrics might be most unfamiliar to the student brought up in the world of spreadsheets and click-and-press computer packages. Accordingly, in addit
ion
to discussing computat
ion
in detail in the book itself, the website associated with the book contains MATLAB programs for performing
Bayesian
analysis in a wide variety of models. In
general
, the focus of the book is on applicat
ion
rather than theory. Hence, I expect that the applied economist
int
erested in using
Bayesian
methods
will find it more useful than the theoretical econometrician.
pattern recognit
ion
and machine learning
The dramatic growth in practical applicat
ion
s for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example,
Bayesian
methods
have grown from a specialist niche to become mainstream, while graphical models have emerged as a
general
framework for describing and applying probabilistic techniques. The practical applicability of
Bayesian
methods
has been greatly enhanced by the development of a range of approximate inference algorithms such as variat
ion
al Bayes and expectat
ion
propagat
ion
, while new models based on kernels have had a significant impact on both algorithms and applicat
ion
s. This completely new textbook reflects these recent developments while providing a comprehensive
int
roduct
ion
to the fields of pattern recognit
ion
and machine learning. It is aimed at advanced undergraduates……
蒙特卡罗数学建模
《蒙特卡罗统计方法(第2版)(英文版)》内容简介:
Int
roduct
ion
、Statistical Models、Likelihood
Methods
、
Bayesian
Methods
、Deterministic Numerical
Methods
、Optimizat
ion
、
Int
egrat
ion
、Comparison、Problems、Notes、Prior Distribut
ion
s、Bootstrap
Methods
、Random Variable Generat
ion
、
Int
roduct
ion
、Uniform Simulat
ion
、The Inverse Transform、Alternatives、Optimal Algorithms、
General
Transformat
ion
Methods
、Accept-Reject
Methods
、The Fundamental Theorem of Simulat
ion
、The Accept-Reject Algorithm、Envelope Accept-Reject
Methods
、The Squeeze Principle、Log-Concave Densities等等。
Pattern Recognit
ion
and Machine Learning高清pdf
The dramatic growth in practical applicat
ion
s for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example,
Bayesian
methods
have grown from a specialist niche to become mainstream, while graphical models have emerged as a
general
framework for describing and applying probabilistic techniques. The practical applicability of
Bayesian
methods
has been greatly enhanced by the development of a range of approximate inference algorithms such as variat
ion
al Bayes and expectat
ion
propagat
ion
, while new models based on kernels have had a significant impact on both algorithms and applicat
ion
s. This completely new textbook reflects these recent developments while providing a comprehensive
int
roduct
ion
to the fields of pattern recognit
ion
and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practit
ion
ers. No previous knowledge of pattern recognit
ion
or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained
int
roduct
ion
to basic probability theory. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vis
ion
, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solut
ion
s for a subset of the exercises are available from the book web site, while solut
ion
s for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of addit
ion
al material, and the reader is encouraged to visit the book web site for the latest informat
ion
.
下载资源悬赏专区
13,656
社区成员
12,674,803
社区内容
发帖
与我相关
我的任务
下载资源悬赏专区
CSDN 下载资源悬赏专区
复制链接
扫一扫
分享
社区描述
CSDN 下载资源悬赏专区
其他
技术论坛(原bbs)
社区管理员
加入社区
获取链接或二维码
近7日
近30日
至今
加载中
查看更多榜单
社区公告
暂无公告
试试用AI创作助手写篇文章吧
+ 用AI写文章