Generalized Probability Smoothing

نویسنده

  • Christopher Mattern
چکیده

In this work we consider a generalized version of Probability Smoothing, the core elementary model for sequential prediction in the state of the art PAQ family of data compression algorithms. Our main contribution is a code length analysis that considers the redundancy of Probability Smoothing with respect to a Piecewise Stationary Source. The analysis holds for a finite alphabet and expresses redundancy in terms of the total variation in probability mass of the stationary distributions of a Piecewise Stationary Source. By choosing parameters appropriately Probability Smoothing has redundancyO(S · √ T logT) for sequences of length T with respect to a Piecewise Stationary Source with S segments.

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Use of Two Smoothing Parameters in Penalized Spline Estimator for Bi-variate Predictor Non-parametric Regression Model

Penalized spline criteria involve the function of goodness of fit and penalty, which in the penalty function contains smoothing parameters. It serves to control the smoothness of the curve that works simultaneously with point knots and spline degree. The regression function with two predictors in the non-parametric model will have two different non-parametric regression functions. Therefore, we...

متن کامل

Analysis of the Posterior for

A \partially improper" Gaussian prior is considered for Bayesian inference in logistic regression. This includes generalized smoothing spline priors that are used for nonparametric inference about the logit, and also priors that correspond to generalized linear mixed models. Necessary and su cient conditions are given for the posterior to be a proper probability measure, and bounds are given fo...

متن کامل

Partially Improper Gaussian Priors for Nonparametric Logistic Regression

A \partially improper" Gaussian prior is considered for Bayesian inference in logistic regression. This includes generalized smoothing spline priors that are used for nonparametric inference about the logit, and also priors that correspond to generalized random e ect models. Necessary and su cient conditions are given for the posterior to be a proper probability measure, and bounds are given fo...

متن کامل

Computing Term Translation Probabilities with Generalized Latent Semantic Analysis

Term translation probabilities proved an effective method of semantic smoothing in the language modelling approach to information retrieval tasks. In this paper, we use Generalized Latent Semantic Analysis to compute semantically motivated term and document vectors. The normalized cosine similarity between the term vectors is used as term translation probability in the language modelling framew...

متن کامل

Differential equation models for statistical functions

Differential equations have been used in statistics to define functions such as probability densities. But the idea of using differential equation formulations of stochastic models has a much wider scope. The author gives several examples, including simultaneous estimation of a regression model and residual density, monotone smoothing, specification of a link function, differential equation mod...

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

عنوان ژورنال:
  • CoRR

دوره abs/1712.02151  شماره 

صفحات  -

تاریخ انتشار 2017