Markov chain sampling schemes generate dependent observations {Θ i, 0 ≤ i ≤ n} from a full joint posterior distribution π(θ∣data). Frequently, only certain marginals of this full posterior density are ...
Although discrete mixture modelling has formed the backbone of the literature on Bayesian density estimation, there are some well-known disadvantages. As an alternative to discrete mixtures, we ...
Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...
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