Statistics and Computing

Aims & Scope

Statistics and Computing is a bi-monthly refereed journal which publishes papers covering the range of the interface between the statistical and computing sciences.

In particular, it addresses the use of statistical concepts in computing science, for example in machine learning, computer vision and data analytics, as well as the use of computers in data modelling, prediction and analysis.

Specific topics which are covered include: techniques for evaluating analytically intractable problems such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification.

In addition, the journal contains original research reports, authoritative review papers, discussed papers, and occasional special issues on particular topics or carrying proceedings of relevant conferences.

Statistics and Computing also publishes book review and software review sections.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.6
2024	1.60

Journal Citation Indicator

Year	Value
2024	672

SJR (SCImago Journal Rank)

Year	Value
2024	0.815

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	89

Journal Rank

Year	Value
2024	6604

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Decision Sciences and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A tutorial on support vector regression

  Citation: 9156

  Authors: Alex J., Bernhard

• A tutorial on spectral clustering

  Citation: 7392

  Authors: Ulrike

• WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility

  Citation: 4262

  Authors: David J., Andrew, Nicky, David

• Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

  Citation: 3772

  Authors: Aki, Andrew, Jonah

• On sequential Monte Carlo sampling methods for Bayesian filtering

  Citation: 3157

  Authors: Arnaud, Simon, Christophe

• A genetic algorithm tutorial

  Citation: 3071

  Authors: Darrell

• Understanding predictive information criteria for Bayesian models

  Citation: 1542

  Authors: Andrew, Jessica, Aki

• DRAM: Efficient adaptive MCMC

  Citation: 1267

  Authors: Heikki, Marko, Antonietta, Eero

• Genetic programming as a means for programming computers by natural selection

  Citation: 1195

  Authors: JohnR.

• ADE-4: a multivariate analysis and graphical display software

  Citation: 1184

  Authors: Jean, Daniel, Sylvain, Jean-Michel

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