... | ... | @@ -2,7 +2,7 @@ |
|
|
|
|
|
`salza` is a practical implementation of two universal algorithmic information measures on sequences based on LZ77[^1]<sup>,</sup>[^2] and relative LZ[^3]<sup>,</sup>[^4] coding.
|
|
|
|
|
|
Its rationale are described in this [preprint](https://cloud.uvolante.org/index.php/s/bJrf25qXQKEFxmZ/download). We thank the anonymous reviewers of ISIT'19 for spotting an inconsistency in the DPI. This version is an update to the one submitted to IT, reflecting the current implementation (faster and slightly more accurate) and illustrating universal causal inference.
|
|
|
Its rationale are described in this [preprint](https://cloud.uvolante.org/index.php/s/bJrf25qXQKEFxmZ/download). We thank the anonymous reviewers of ISIT'19 for spotting an inconsistency in the DPI ([ISIT'19 slides](https://cloud.uvolante.org/index.php/s/7oSeZKy9GKAMKKg)). This version is an update to the one submitted to IT, reflecting the current implementation (faster and slightly more accurate) and illustrating universal causal inference.
|
|
|
|
|
|
`salza` comes with built-in computation of a universal, normalized semi-distance (much in the spirit of Cilibrasi _et al._ work[^5]) and an implementation of causality inference using the (stable) PC algorithm[^6].
|
|
|
|
... | ... | |