Space alternating variational estimation based sparse Bayesian learning for complex‐value sparse signal recovery using adaptive Laplace priors

Abstract

Due to its self‐regularising nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most existing methods are based on the real‐value signal model, with the complex‐value signal model rarely considered. Motivated by the adaptive least absolute shrinkage and selection operator (LASSO) and the sparse Bayesian learning framework, a hierarchical model with adaptive Laplace priors is proposed in this paper for recovery of complex sparse signals. Moreover, the space alternating approach is integrated into the algorithm to reduce the computational complexity of the proposed method. In experiments, the proposed algorithm is studied for complex Gaussian random dictionaries and different types of complex signals. These experiments show that the proposed algorithm offers better recovery performance for different types of complex signals than state‐of‐the‐art methods.

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

“…The conceptual framework of the HM work flow is depicted in Figure 3. A Bayesian model is a statistical model in which probability is used to represent all uncertainty inside the model [39], including uncertainty about the output as well as uncertainty about the model's input (parameters). The underlying information is described as a prior distribution and paired with observational data in the form of a likelihood function to obtain the posterior distribution.…”

“…The HM combines several distinct models [37]. Better security is provided by the hybrid ML approach, which also seeks less computational training time [38,39]. Building reliable computer models to predict changes in GWLs using high-throughput data is becoming more and more popular.…”