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Journal Articles Electronic Journal of Statistics Year : 2018

Normalizing constants of log-concave densities


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 (Durmus, A. and Moulines, E. (2016). High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm). 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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Dates and versions

hal-01648666 , version 1 (27-11-2017)
hal-01648666 , version 2 (06-03-2018)



Nicolas Brosse, Alain Durmus, Éric Moulines. Normalizing constants of log-concave densities. Electronic Journal of Statistics , 2018, 12 (1), pp.851-889. ⟨10.1214/18-EJS1411⟩. ⟨hal-01648666v2⟩
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