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Adaptive independent sticky Metropolis (AISM) algorithm

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An adaptive Metropolis sampler for draw from any bounded univariate target distributions

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The adaptive independent sticky Metropolis (AISM) sampler is an algorithm to draw efficiently from any (bounded) univariate target distribution.
The proposal density is non-parametric and the construction procedure relies upon alternative interpolation strategies. The user can control the L1 distance between the proposal and the target pdf (i.e., the convergence of the proposal to the target) through the design of suitable statistical update test, which also controls the overall computational cost.
The adjective ``sticky'' highlights the ability of the proposed schemes to generate a sequence of proposal densities that progressively ``stick'' to the target.

See the examples in MAIN.m for using properly the code.

For further technical information, see

L. Martino, R. Casarin, F. Leisen, D. Luengo. Adaptive Independent Sticky MCMC algorithms, arXiv:1308.3779, 2016.

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