Analysis of spike train data: classification and Bayesian alignment

DOI10.1214/14-EJS865CzbMath1305.62328OpenAlexW2031321629WikidataQ58419210 ScholiaQ58419210MaRDI QIDQ470453

Huiling Le, David B. Hitchcock, Wen Cheng, Ian L. Dryden

Publication date: 12 November 2014

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1414588163

zbMATH Keywords

Markov chain Monte CarloPoisson processregistrationtime warping

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Inference from stochastic processes and spectral analysis (62M15) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (2)

Analysis of spike train data: alignment and comparisons using the extended Fisher-Rao metric ⋮ Analysis of spike train data: discussion of results

Cites Work

• Miscellanea Kernel-Type Density Estimation on the Unit Interval
• Analysis of proteomics data: Bayesian alignment of functions
• Introduction to neural spike train data for phase-amplitude analysis
• Bayesian registration of functions and curves

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