Adaptive independent sticky MCMC algorithms

EURASIP Journal on Advances in Signal Processing

Table 8 (Ex-Sect-9.4.1). Mean absolute error (MAE) in the estimation of four statistics (first component) and normalized computing time (Continued)

Technique	T	N _G	Init.	MAE				Avg. MAE	Time
				Mean	Variance	Skewness	Kurtosis
MH (σ_p=10)	1	10000	In2	0.178	0.126	0.091	0.012	0.102	0.162
		20000		0.151	0.112	0.090	0.008	0.090	0.331
		30000		0.138	0.063	0.068	0.007	0.069	0.492
	2	10000		0.130	0.062	0.066	0.006	0.066	0.196
	3			0.125	0.066	0.063	0.006	0.065	0.223
	10	2000		0.149	0.083	0.075	0.009	0.079	0.081
Adaptive MH	10	2000	In2	0.158	0.082	0.087	0.012	0.084	0.090
	100			0.146	0.076	0.073	0.010	0.076	0.634
HMC	10	2000	In2	0.152	0.092	0.079	0.015	0.084	0.092
	100			0.148	0.081	0.070	0.012	0.077	0.630
Slice	3	2000	In2	0.204	0.105	0.103	0.022	0.108	0.156
	10			0.188	0.091	0.095	0.018	0.098	0.463
	3	10000		0.132	0.051	0.066	0.007	0.064	0.783

All the techniques are used within a Gibbs sampler: N_G is the number of iterations of the Gibbs sampler whereas T is is the number of iterations of the technique within Gibbs (so that T×N_G is the global number of MCMC iterations). The best results (in each column, and in each panel) are highlighted with italics