On identifiability and information-regularity in parametrized normal distributions

Bertrand Hochwald, Arye Nehorai

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


We describe methods to establish identifiability and information-regularity of parameters in normal distributions. Parameters are considered identifiable when they are determined uniquely by the probability distribution and they are information-regular when their Fisher information matrix is full rank. In normal distributions, information-regularity implies local identifiability, but the converse is not always true. Using the theory of holomorphic mappings, we show when the converse is true, allowing information-regularity to be established without having to explicitly compute the information matrix. Some examples are given.

Original languageEnglish
Pages (from-to)83-89
Number of pages7
JournalCircuits, Systems, and Signal Processing
Issue number1
StatePublished - 1997


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