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Knowing the statistical properties of a complex-valued signal is important in many signal processing applications by providing the necessary information for choosing the appropriate algorithm. In this paper, we provide generalized likelihood ratio tests (GLRT), based on the complex generalized Gaussian distribution (CGGD), for detecting two important signal properties: 1) the circularity of a complex...
In this paper, a new class of lower bounds on the probability of error for m-ary hypothesis tests is proposed. Computation of the minimum probability of error which is attained by the maximum a-posteriori probability (MAP) criterion, is usually not tractable. The new class is derived using Holder??s inequality. The bounds in this class are continuous and differentiable function of the conditional...
Bayesian marginalization is shown to measure the complexity of a model, objectively quantifying Ockham's Razor via the Ockham parameter inference (OPI). This is not possible in likelihood-based inference. The OPI rejects any hypothesis which is poorly supported by the data. This leads to the Censored Marginal A Posteriori (CMaAP) estimation policy which returns confident estimates well below the maximum...
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