Structures of confinement in nineteenth-century asylums, using England and Ontario as a comparative study

Traditionally, historians of the care of the insane have understood their work as a branch of medical history. This paper focuses instead on the administrative structures of nineteenth century asylums. These are geographically specific and historically contingent. The development of medico-legal discourse will depend on localized histories of medicine and law in individual jurisdictions concerned. In this paper, the legal structures of public asylums in Ontario and England in the mid-nineteenth century are taken as a case study of this approach. Consideration of the differences in administrative structures challenges the degree to which the institutions were understood in the same way in the nineteenth century, and can be understood as comparable by historians today: is it appropriate to refer to ‘the asylum’ as a coherent and consistent concept between jurisdictions in the nineteenth century. The answer may well be in the affirmative, but it will become clear that differences in administrative structures are significant, and as instructive as similarities

Sex, dementia, capacity and care homes

This paper addresses the appropriate legal and policy approach to sexual conduct involving people with dementia in care homes, where the mental capacity of one or both partners is compromised. Such conduct is prohibited by sections 34–42 of the Sexual Offences Act 2003, but this article asks whether this blanket prohibition is necessarily the appropriate response. The article considers a variety of alternative responses, eventually arguing that clearer guidance regarding prosecution should be issued

Convergence of Langevin MCMC in KL-divergence

Langevin diffusion is a commonly used tool for sampling from a given distribution. In this work, we establish that when the target density $p^*$ is such that $\log p^*$ is $L$ smooth and $m$ strongly convex, discrete Langevin diffusion produces a distribution $p$ with $KL(p||p^*)\leq \epsilon$ in $\tilde{O}(\frac{d}{\epsilon})$ steps, where $d$ is the dimension of the sample space. We also study the convergence rate when the strong-convexity assumption is absent. By considering the Langevin diffusion as a gradient flow in the space of probability distributions, we obtain an elegant analysis that applies to the stronger property of convergence in KL-divergence and gives a conceptually simpler proof of the best-known convergence results in weaker metrics

Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families

We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction strategy with Jeffreys prior and sequential normalized maximum likelihood (SNML) coincide and are optimal if and only if the latter is exchangeable, and if and only if the optimal strategy can be calculated without knowing the time horizon in advance. They put forward the question what families have exchangeable SNML strategies. This paper fully answers this open problem for one-dimensional exponential families. The exchangeability can happen only for three classes of natural exponential family distributions, namely the Gaussian, Gamma, and the Tweedie exponential family of order 3/2. Keywords: SNML Exchangeability, Exponential Family, Online Learning, Logarithmic Loss, Bayesian Strategy, Jeffreys Prior, Fisher Information1Comment: 23 page

Optimal Allocation Strategies for the Dark Pool Problem

We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem to adversarial scenarios, while also improving on their results in the iid setup. The resulting algorithms are efficient, and perform well in simulations under stochastic and adversarial inputs