Field Theories for Learning Probability Distributions

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Field Theories for Learning Probability Distributions.

Imagine being shown N samples of random variables drawn independently from the same distribution. What can you say about the distribution? In general, of course, the answer is nothing, unless you have some prior notions about what to expect. From a Bayesian point of view one needs an a priori distribution on the space of possible probability distributions, which defines a scalar field theory. I...

متن کامل

Learning Probability Distributions

Learning Probability Distributions

متن کامل

Methods of Data Analysis Learning probability distributions

One of the key problems in non-parametric data analysis is to infer good models of probability distributions, assuming we are given as data a finite sample from that distribution. This problem is ill-posed for continuous distributions, even when they are low-dimensional: with finite data, there is no way to distinguish between (or exclude) distributions that are not regularized a priori, for ex...

متن کامل

On Probability Distributions for Trees: Representations, Inference and Learning

We study probability distributions over free algebras of trees. Probability distributions can be seen as particular (formal power) tree series [BR82; EK03], i.e. mappings from trees to a semiring K. A widely studied class of tree series is the class of rational (or recognizable) tree series which can be defined either in an algebraic way or by means of multiplicity tree automata. We argue that ...

متن کامل

Performance guarantees for kernel-based learning on probability distributions∗

In this talk I will present a novel result concerning the theory of distribution regression (DR). The DR problem addresses regression to vector-valued outputs from probability measures. Many important machine learning and statistical tasks fit into this framework, including multi-instance learning, and point estimation problems without analytical solution. Despite the large number of available ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Physical Review Letters

سال: 1996

ISSN: 0031-9007,1079-7114

DOI: 10.1103/physrevlett.77.4693