Bayesian Analysis

Top Research Topics at Bayesian Analysis?

The discussions in Bayesian Analysis mainly cover the fields of Bayesian probability, Bayesian inference, Prior probability, Artificial intelligence and Algorithm. In addition to Bayesian probability research, the journal aims to explore topics under Inference and Econometrics. The Bayesian inference study featured in Bayesian Analysis draws connections with the study of Bayes' theorem.

While the primary focus in it is Prior probability, it also dissects topics surrounding Applied mathematics and Dirichlet process as a whole. The work on Artificial intelligence tackled in the journal brings together disciplines like Machine learning and Pattern recognition. While work presented in Bayesian Analysis provided substantial information on Algorithm, it also covered topics in Markov chain and Gibbs sampling.

Some problems in Markov chain Monte Carlo that were presented in Bayesian Analysis overlapped with concepts under Sampling (statistics) and Mathematical optimization. The journal primarily discusses Statistics topics, particularly Bayesian linear regression, Covariate and Estimator. Bayesian Analysis facilitates discussions on Bayesian linear regression that incorporate concepts from other fields like Bayesian hierarchical modeling and Bayesian statistics.

• Bayesian probability (41.58%)
• Bayesian inference (22.02%)
• Prior probability (21.63%)

What are the most cited papers published in the journal?

• Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) (2552 citations)
• Flexible paleoclimate age-depth models using an autoregressive gamma process (1636 citations)
• Variational Inference for Dirichlet Process Mixtures (1105 citations)

Research areas of the most cited articles at Bayesian Analysis:

The most cited papers aim to foster the development of research in Bayesian probability, Artificial intelligence, Markov chain Monte Carlo, Bayesian inference and Algorithm. While the most cited articles focused on Bayesian probability, they were also able to explore topics like Econometrics and Applied mathematics. The most cited articles explore topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning and Pattern recognition.

What topics the last edition of the journal is best known for?

• Statistics
• Normal distribution
• Artificial intelligence

The previous edition focused in particular on these issues:

The scientific interests tackled in Bayesian Analysis are Bayesian probability, Prior probability, Algorithm, Bayesian inference and Inference. The study of Bayesian probability, which falls within the realm of Artificial intelligence, was the main focus of the presentations. The featured Prior probability study falls within the wider topic of Statistics.

While Algorithm is the key highlight in it, it also covered some subjects on Sampling (statistics) and Predictive inference. Bayesian inference is the main point of discussion in the journal but it also connects with fields such as

• Statistical model which intersects with area such as Bayesian experimental design, Design of experiments and Mathematical optimization,
• Multivariate statistics which is related to area like Econometrics.. Topics in Inference explored in Bayesian Analysis were investigated in conjunction with research in Scalability, Data mining and Bayes' theorem.

The most cited articles from the last journal are:

• Rank-Normalization, Folding, and Localization: An Improved $\widehat{R}$ for Assessing Convergence of MCMC (76 citations)
• Likelihood-free inference by ratio estimation (17 citations)
• Parallel Gaussian Process Surrogate Bayesian Inference with Noisy Likelihood Evaluations (15 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in Bayesian Analysis (based on the number of publications) are:

• Christopher C. Drovandi (11 papers) published 4 papers at the last edition,
• Fernando A. Quintana (11 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• David B. Dunson (10 papers) published 1 paper at the last edition the same number as at the previous edition,
• Peter Müller (10 papers) absent at the last edition,
• Michael West (9 papers) published 1 paper at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Bayesian Analysis (based on the number of publications) are:

• University of Texas at Austin (19 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Duke University (17 papers) published 2 papers at the last edition,
• University of Texas MD Anderson Cancer Center (15 papers) published 2 papers at the last edition,
• Harvard University (11 papers) published 3 papers at the last edition, 2 more than at the previous edition,
• University of Toronto (10 papers) published 3 papers at the last edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 19.77% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 30.43% were posted by at least one author from the top 10 institutions publishing in the journal. Another 23.19% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.94% of all publications and 30.43% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.