Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

Approximate Bayesian computation (ABC) is a popular technique for analysing data for complex models where the likelihood function is intractable. It involves using simulation from the model to approximate the likelihood, with this approximate likelihood then being used to construct an approximate posterior. In this paper, we consider methods that estimate the parameters by maximizing the approximate likelihood used in ABC. We give a theoretical analysis of the asymptotic properties of the resulting estimator. In particular, we derive results analogous to those of consistency and asymptotic normality for standard maximum likelihood estimation. We also discuss how sequential Monte Carlo methods provide a natural method for implementing our likelihood-based ABC procedures.

Spinning off a semiconductor company from University premises -The story of Camsemi

The spinning off of Cambridge Semiconductor Ltd (Camsemi) from the High Voltage Microelectronics Lab at Cambridge University is discussed. The technology originated from Cambridge University and was subsequently developed and commercialized as PowerBrane by Camsemi. The paper also discusses the business model and the enabling financial factors that led to the formation of Camsemi as a fables IC company, including access to seed funding from University and the subsequent investments of venture capital in several rounds. © 2011 IEEE.