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  • 标题:Normalizing constants of log-concave densities
  • 本地全文:下载
  • 作者:Nicolas Brosse ; Alain Durmus ; Éric Moulines
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2018
  • 卷号:12
  • 期号:1
  • 页码:851-889
  • DOI:10.1214/18-EJS1411
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要:We derive explicit bounds for the computation of normalizing constants $Z$ for log-concave densities $\pi =\mathrm{e}^{-U}/Z$ w.r.t. the Lebesgue measure on $\mathbb{R}^{d}$. Our approach relies on a Gaussian annealing combined with recent and precise bounds on the Unadjusted Langevin Algorithm [15]. Polynomial bounds in the dimension $d$ are obtained with an exponent that depends on the assumptions made on $U$. The algorithm also provides a theoretically grounded choice of the annealing sequence of variances. A numerical experiment supports our findings. Results of independent interest on the mean squared error of the empirical average of locally Lipschitz functions are established.
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