INLA or MCMC? A Tutorial and Comparative Evaluation for Spatial Prediction in log-Gaussian Cox Processes

We investigate two options for performing Bayesian inference on spatial log-Gaussian Cox processes assuming a spatially continuous latent field: Markov chain Monte Carlo (MCMC) and the integrated nested Laplace approximation (INLA). We first describe the device of approximating a spatially continuous Gaussian field by a Gaussian Markov random field on a discrete lattice, and present a simulation study showing that, with careful choice of parameter values, small neighbourhood sizes can give excellent approximations. We then introduce the spatial log-Gaussian Cox process and describe MCMC and INLA methods for spatial prediction within this model class. We report the results of a simulation study in which we compare MALA and the technique of approximating the continuous latent field by a discrete one, followed by approximate Bayesian inference via INLA over a selection of 18 simulated scenarios. The results question the notion that the latter technique is both significantly faster and more robust than MCMC in this setting; 100,000 iterations of the MALA algorithm running in 20 minutes on a desktop PC delivered greater predictive accuracy than the default \verb=INLA= strategy, which ran in 4 minutes and gave comparative performance to the full Laplace approximation which ran in 39 minutes.Comment: This replaces the previous version of the report. The new version includes results from an additional simulation study, and corrects an error in the implementation of the INLA-based method

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Students with Autism Spectrum Disorders Who Participate in FIRST Robotics

One of the challenges of educating adolescents with autism spectrum disorders is to find activities that are interesting and engaging. Researchers have shown that adolescents with autism often are attracted to technology. Using an exploratory research method, the experiences of three students with autism who participated in after school robotics clubs were analyzed. Common themes to emerge were the students with ASD were engaged and interested in FIRST robotics and technology. Specifically, they enjoyed coming up with ideas, inventions, and creating or solving missions. Their confidence increased and they learned to work better on teams. Additionally, they needed significant supports to participate in FIRST Robotics