This chapter presents an overview of Multicultural London English (MLE), the urban contact vernacular that has emerged in London in recent years. It starts with a discussion of how similar varieties have been reported across other European cities and have become known as multiethnolects, meaning that they are not restricted to any particular ethnic group but are available to anyone, including speakers from non-immigrant backgrounds. The chapter then focuses on the specific social and historical circumstances that have led to the emergence of MLE, from its beginnings in the 1980s to the present day. After presenting the linguistic characteristics of MLE, a discussion follows of the ways in which MLE has been perceived in the media and by users and non-users of MLE, and how attitudes towards the variety may influence its trajectory in the future. While there is some suggestion that the variety (or some variation thereof) may not be restricted to London, it is not clear whether MLE will stabilise to an everyday vernacular spoken in inner-city neighbourhoods and beyond or whether it will divide along social and ethnic lines. The chapter concludes with a discussion of new research being undertaken to answer some of these issues.

Suppose one has a set of data that arises from a specific distribution with unknown parameter vector. A natural question to ask is the following: what value of this vector is most likely to have generated these data? The answer to this question is provided by the maximum-likelihood estimator (MLE). Likelihood and related functions are the subject of this chapter. It will turn out that we have already seen some examples of MLEs in the previous chapters. Here, we define likelihood, the score vector, the Hessian matrix, the information-matrix equivalence, parameter identification, the Cramér–Rao lower bound and its extensions, profile (concentrated) likelihood and its adjustments, as well as the properties of MLEs (including conditions for existence, consistency, and asymptotic normality) and the score (including martingale representation and local sufficiency). Applications are given, including some for the normal linear model.