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The importance of conference!

The author discusses the benefits of attending conferences for physical education teachers. It is noted that within and outside of the teaching profession physical educators are sometimes not viewed as teachers and the author believes that creating professional development opportunities will change that

The Importance of Needs Analysis in Materials Development

There is an issue which can be the subject of a further research that is: most learners of EFL do not actually have any needs at all. They are learning English because they have to rather than because they want or need to. This is particularly true of young learners, who are not going to need to communicate in English for many years to come (if at all). In such cases, the teacher is unlikely to be able to create much intrinsic motivation to learn English in general but can create the need in an engaging classroom task in which the students need to find a way of communicating in English in order to successfully complete the task. This can lead to readiness for acquisition provided the students are motivated by the teacher to read extensively as well. Thus, there should be more place for needs analysis because when we do not run the first principle to create and produce better materials, the rest will always be under great doubt. Every new teacher should consider starting their year applying needs analysis in small scales and then decide what the best is for themselves and their learners

Particle Efficient Importance Sampling

The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integrands to build variance minimising importance samplers. Despite a number of successful applications in high dimensions, it is well known that importance sampling strategies are subject to an exponential growth in variance as the dimension of the integration increases. We solve this problem by recognising that the EIS framework has an offline sequential Monte Carlo interpretation. The particle EIS method is based on non-standard resampling weights that take into account the look-ahead construction of the importance sampler. We apply the method for a range of univariate and bivariate stochastic volatility specifications. We also develop a new application of the EIS approach to state space models with Student's t state innovations. Our results show that the particle EIS method strongly outperforms both the standard EIS method and particle filters for likelihood evaluation in high dimensions. Moreover, the ratio between the variances of the particle EIS and particle filter methods remains stable as the time series dimension increases. We illustrate the efficiency of the method for Bayesian inference using the particle marginal Metropolis-Hastings and importance sampling squared algorithms