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Dunlop, M. M. (2019). Multiplicative Noise in Bayesian Inverse Problems: Well-Posedness and Consistency of MAP Estimators. arXiv Preprint.
https://arxiv.org/abs/1910.14632

has been cited by the following article:

• TITLE: Multiplicative Normal Noise and Nonconcavity in the Value of Information

  AUTHORS: Stefan Behringer

  KEYWORDS: Value of Information, Inverse Problems, Bayesian Learning, Monopoly, Multiplicative Noise, Nonconcavity

  JOURNAL NAME: Theoretical Economics Letters, Vol.11 No.1, February 25, 2021

  ABSTRACT: This paper investigates a Bayesian inverse problem of a price setting monopolist facing a random demand. In contrast to previous investigations an unknown true market potential of demand is distorted by two independent Gaussian errors, a zero-mean additive and a unity-mean multiplicative one. The multi-period game allows for learning from realized market demands (signals). Interestingly increasing the level of noise of a multiplicative error in this dynamic setting can actually improve the Value of Information of signals to the firm, a result that cannot hold for a single additive error or in a static context.