基本信息
浏览量:399
职业迁徙
个人简介
I view statistics as the general science of empirical learning, which can be used to support scientific inference, to solve engineering problems, and to clarify how natural and artificial agents can learn from experience.
Statistics is not always easy. It raises many interesting philosophical issues, and often requires the solution of difficult computational problems. Here are some problems and solutions of particular interest to me:
Bayesian inference
An approach to statistics in which all forms of uncertainty are expressed in terms of probability.
Markov chain Monte Carlo
A way of computing high-dimensional integrals that is crucial for doing Bayesian inference.
Neural networks
Statistical models that are relevant to, or at least inspired by, the way learning and computation may occur in the brain.
Latent variable models
Models phrased in terms of entities that we have invented to explain patterns we see in observable variables.
Evaluation of learning methods
Ways of telling which methods for learning from data really work.
Data compression
Using models for data to find a compressed representation of it.
Error correcting codes
Representing information in a redundant form that allows errors to be corrected with high probability.
Statistical applications
I have worked on various statistical applications, mostly of a biological nature.
I also have current, dormant, or possible future interests in philosophy of science, artificial life, programming languages, user interface design, and who knows what else...
Statistics is not always easy. It raises many interesting philosophical issues, and often requires the solution of difficult computational problems. Here are some problems and solutions of particular interest to me:
Bayesian inference
An approach to statistics in which all forms of uncertainty are expressed in terms of probability.
Markov chain Monte Carlo
A way of computing high-dimensional integrals that is crucial for doing Bayesian inference.
Neural networks
Statistical models that are relevant to, or at least inspired by, the way learning and computation may occur in the brain.
Latent variable models
Models phrased in terms of entities that we have invented to explain patterns we see in observable variables.
Evaluation of learning methods
Ways of telling which methods for learning from data really work.
Data compression
Using models for data to find a compressed representation of it.
Error correcting codes
Representing information in a redundant form that allows errors to be corrected with high probability.
Statistical applications
I have worked on various statistical applications, mostly of a biological nature.
I also have current, dormant, or possible future interests in philosophy of science, artificial life, programming languages, user interface design, and who knows what else...
研究兴趣
论文共 109 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
A Guleria,Ahrah Koh, E Whetton,B A Mcgrath,Ciaran C Doherty,D Atkinson, I A Bruce, R Perkins,Radford M Neal, Nicholas W Bateman,Jeffrey S Russell,John P Cooke,
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn