The Effect of Learning Experiences and Context on Infant Imitation and Generalization

Over the first years of life, infants gradually develop the ability to retrieve their memories across cue and contextual changes. Whereas maturational factors drive some of these developments in memory ability, experiences occurring within the learning event may also impact infants' ability to retrieve memories in new situations. In 2 experiments we examined whether it was possible to facilitate 12‐month‐old infants' generalization of learning in the deferred imitation paradigm by varying experiences before or during the demonstration session, or during the retention interval. In Experiment 1, altering the length, timing, or variability of training had no impact on generalization; infants showed a low, but consistent level of memory retrieval. In Experiment 2, infants who experienced a unique context for encoding and retrieval exhibited generalization; infants who experienced the context prior to the demonstration session, or during the retention interval, did not. Specificity is a robust feature of infant memory and is not substantially altered by encoding experiences in an observational learning paradigm. Previous history with a learning environment can, however, impact the flexibility of memory retrieval.     

A spatiotemporal auto-regressive moving average model for solar radiation

Summary.  To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.     

Statistical consulting

The paper and the special issue focus on the activity of statistical consulting and its varieties. This includes academic consulting, consulting to and in industry as well as statistics in public media.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

A simulation study of a discrete-time multiserver retrial queue with finite population

In this paper, we deal with a discrete-time multiserver retrial queue with finite population. Firstly, we study the Markov chain at the epochs immediately after slot boundaries making emphasis on the computation of its steady-state distribution. Then, the main performance measures are investigated. Besides, we simulate the waiting time of a customer in the retrial group under three different queueing policies. Some numerical examples are given to illustrate the analysis.     

Optimal scaling of Metropolis algorithms: Heading toward general target distributions

The authors provide an overview of optimal scaling results for the Metropolis algorithm with Gaussian proposal distribution. They address in more depth the case of high‐dimensional target distributions formed of independent, but not identically distributed components. They attempt to give an intuitive explanation as to why the well‐known optimal acceptance rate of 0.234 is not always suitable. They show how to find the asymptotically optimal acceptance rate when needed, and they explain why it is sometimes necessary to turn to inhomogeneous proposal distributions. Their results are illustrated with a simple example.