
## Sparse Markov Models for High-dimensional Inference
Guilherme Ost, Daniel Y. Takahashi
Keywords: 
JMLR/2023/Proceedings/220266 - Sparse Markov Models for High-dimensional Inference.pdf
Project URL: nan

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[9]

The authors use an implementation from other authors but they do link it directly. The code there is extremely cryptic and its not clear what is used for what, no installation notes. The timestamping also does not make sense with the publication date/year. The link the authors state 'we used the code available from' is not the code from this paper.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[2]

(2/2)

In experiment 1 they use simulated data and provide detailed descriptions and values for it. They provide a GitHub link for the simulator. The authors use a Kaggle dataset in 4.3 and provide a direct link and a brief description but more information would be welcome.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[6]

The uahotrs define various parameter values for each experiment but its not clear what is considered a parameter for their method as an overview is missing (various variables defined in algorithm 1, but no input parameters). Thus its ambigous. No acquisition specified.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The authors present standard deviation and estimated probability. Data split not applicable. Metrics are calculated over various sample sizes n. 

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[8]

Requries expertise on markov chains.
