18 citations · 37 across the 7 of their papers we have counts for
6 papers · 1 filter
Generalized Probability Smoothing
Christopher Mattern
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…
On Probability Estimation by Exponential Smoothing
Christopher Mattern
Probability estimation is essential for every statistical data compression algorithm. In practice probability estimation should be adaptive, recent observations should receive a hi…
On Probability Estimation via Relative Frequencies and Discount
Christopher Mattern
Probability estimation is an elementary building block of every statistical data compression algorithm. In practice probability estimation is often based on relative letter frequen…
Combining non-stationary prediction, optimization and mixing for data compression
Christopher Mattern
In this paper an approach to modelling nonstationary binary sequences, i.e., predicting the probability of upcoming symbols, is presented. After studying the prediction model we ev…
Mixing Strategies in Data Compression
Christopher Mattern
We propose geometric weighting as a novel method to combine multiple models in data compression. Our results reveal the rationale behind PAQ-weighting and generalize it to a non-bi…
Linear and Geometric Mixtures - Analysis
Christopher Mattern
Linear and geometric mixtures are two methods to combine arbitrary models in data compression. Geometric mixtures generalize the empirically well-performing PAQ7 mixture. Both mixt…