Manipulation and articulation: population statistics in 19th century France and England

The article presents an essay that deals with the study conducted by Donald MacKenzie and the case studies comparing the use of population statistics in France and Great Britain in the periods of 1825 and 1885. It analyzes Donald MacKenzie's study on the ways professional and political commitments informed the choice of statistical indexes in the British statistical community. Furthermore, the author is interested in knowing how this influenced the development of mathematical statistics in Great Britain. The author concludes that the differences in the debates over population statistics are accounted to the differences in the social and epistemological logics of population statistics.

Bayesian parameter estimation for latent Markov random fields and social networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.