✦ LIBER ✦

Finite-memory algorithms for estimating the mean of a Gaussian distribution (Corresp.)

✍ Scribed by Hellman, M.

Book ID: 114634259
Publisher: IEEE
Year: 1974
Tongue: English
Weight: 601 KB
Volume: 20
Category: Article
ISSN: 0018-9448
DOI: 10.1109/tit.1974.1055229

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Estimation of the mean with time-varying

Estimation of the mean with time-varying finite memory (Corresp.)

✍ Wagner, T. 📂 Article 📅 1972 🏛 IEEE 🌐 English ⚖ 471 KB

On Robust estimates of the mean for Gaus

On Robust estimates of the mean for Gaussian distributions in Hilbert space

✍ I. Sh. Ibramkhalilov 📂 Article 📅 1974 🏛 Springer 🌐 English ⚖ 225 KB

A sequential multiple hypotheses test fo

A sequential multiple hypotheses test for the unknown parameters of a Gaussian distribution (Corresp.)

✍ Fleisher, S.; Shwedyk, E. 📂 Article 📅 1980 🏛 IEEE 🌐 English ⚖ 668 KB

A nonparametric estimation of the entrop

A nonparametric estimation of the entropy for absolutely continuous distributions (Corresp.)

✍ Ahmad, I.; Pi-Erh Lin 📂 Article 📅 1976 🏛 IEEE 🌐 English ⚖ 611 KB

Gaussian and non-Gaussian limit distribu

Gaussian and non-Gaussian limit distributions of estimates of the regression coefficients of a long-memory time series

✍ M. M. Leonenko; M. M. Sharapov 📂 Article 📅 1999 🏛 Springer 🌐 English ⚖ 535 KB

An EM type algorithm for maximum likelih

An EM type algorithm for maximum likelihood estimation of the normal–inverse Gaussian distribution

✍ Dimitris Karlis 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 134 KB

The Normal-Inverse Gaussian distribution arises as a Normal variance-mean mixture with an Inverse Gaussian mixing distribution. This article deals with Maximum Likelihood estimation of the parameters of the Normal-Inverse Gaussian distribution. Due to the complexity of the likelihood, direct maximiz