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The foundation collection of the printed books now forming the Library of the British Museum was that of Sir Hans Sloane. This comprised about 40,000 volumes. To it was added in 1759 the Royal collection, begun in the time of Henry VII and inherited by George II from his predecessors on the throne.

THE recently founded Bulgarian Bibliographical Institute has produced three publications of great interest. The first of these, by Dr. T. Borov, the Director of the Institute and of the National Library of Sofia, Why do we need a Bulgarian Bibliographical Institute?, was published in 1941, before the foundation of the Institute, and, as its sub‐title indicates, is ‘an address to friends’. In it Dr. Borov reviewed briefly the position of Bulgarian bibliography and set forth what he considered should be the aims of the Institute.

In financial markets investors and borrowers are faced with a whole structure of prices and interest rates on financial instruments. The determination of equilibria in these markets is a complex process and presents a challenge to researchers and practitioners alike. In this article we are concerned with a single section of these markets where we study the relationships between the interest rates or yields on financial securities which can be distinguished from each other (as far as possible) only by their term to maturity. We only cover the structure of money or nominal yields, as an examination of the real returns would require another arti‐cle in itself.

The search for flexible models has led the simple multinomial logit model to evolve into the powerful but computationally very demanding mixed multinomial logit (MMNL) model. That flexibility search lead to discrete choice hybrid choice models (HCMs) formulations that explicitly incorporate psychological factors affecting decision making in order to enhance the behavioral representation of the choice process. It expands on standard choice models by including attitudes, opinions, and perceptions as psychometric latent variables.

In this paper we describe the classical estimation technique for a simulated maximum likelihood (SML) solution of the HCM. To show its feasibility, we apply it to data of stated personal vehicle choices made by Canadian consumers when faced with technological innovations.

We then go beyond classical methods, and estimate the HCM using a hierarchical Bayesian approach that exploits HCM Gibbs sampling considering both a probit and a MMNL discrete choice kernel. We then carry out a Monte Carlo experiment to test how the HCM Gibbs sampler works in practice. To our knowledge, this is the first practical application of HCM Bayesian estimation.

We show that although HCM joint estimation requires the evaluation of complex multi-dimensional integrals, SML can be successfully implemented. The HCM framework not only proves to be capable of introducing latent variables, but also makes it possible to tackle the problem of measurement errors in variables in a very natural way. We also show that working with Bayesian methods has the potential to break down the complexity of classical estimation.