Infants’ reasoning about samples generated by intentional versus non‐intentional agents

The current experiments investigate how infants use goal‐directed action to reason about intentionally sampled outcomes in a probabilistic inference paradigm. Older infants and young children are flexible in their expectations of sampling: They expect random samples to reflect population statistics and non‐random samples to reflect an agent's preferences or goals (Kushnir, Xu, & Wellman, 2010; Xu & Denison, 2009). However, more recent work shows that probabilistic inference comes online at approximately 6 months (Denison, Reed, & Xu, 2013; Kayhan, Gredebäck, & Lindskog, 2017; Ma & Xu, 2011; Wellman, Kushnir, Xu, & Brink, 2016), and thus, these sampling assumptions can be investigated at the age probabilistic reasoning first emerges. Results indicate that 6‐month‐old infants expect a human agent to sample in accord with their goal and do not expect the same of an unintentional agent—a mechanical claw. By 9.5 months, infants expect the mechanical claw to sample in accord with random sampling. These results suggest that infants use goals to make inferences about intentional sampling, under appropriate conditions at 6 months, and they have expectations of the kinds of samples a mechanical device should obtain by 9.5 months.     

Boosting with Bayesian stumps

Boosting is a new, powerful method for classification. It is an iterative procedure which successively classifies a weighted version of the sample, and then reweights this sample dependent on how successful the classification was. In this paper we review some of the commonly used methods for performing boosting and show how they can be fit into a Bayesian setup at each iteration of the algorithm. We demonstrate how this formulation gives rise to a new splitting criterion when using a domain-partitioning classification method such as a decision tree. Further we can improve the predictive performance of simple decision trees, known as stumps, by using a posterior weighted average of them to classify at each step of the algorithm, rather than just a single stump. The main advantage of this approach is to reduce the number of boosting iterations required to produce a good classifier with only a minimal increase in the computational complexity of the algorithm.     

Risks of Municipal Solid Waste Incineration: An Environmental Perspective

The central focus of the debate over incineration of municipal solid waste (MSW) has shifted from its apparent management advantages to unresolved risk issues. This shift is a result of the lack of comprehensive consideration of risks associated with incineration. We discuss the need to expand incinerator risk assessment beyond the limited view of incinerators as stationary air pollution sources to encompass the following: other products of incineration, ash in particular, and pollutants other than dioxins, metals in particular; routes of exposure in addition to direct inhalation; health effects in addition to cancer; and the cumulative nature of exposure and health effects induced by many incinerator-associated pollutants. Rational MSW management planning requires that the limitations as well as advantages of incineration be recognized. Incineration is a waste-processing--not a waste disposal--technology, and its products pose substantial management and disposal problems of their own. Consideration of the nature of these products suggests that incineration is ill-suited to manage the municipal wastestream in its entirety. In particular, incineration greatly enhances the mobility and bioavailability of toxic metals present in MSW. These factors suggest that incineration must be viewed as only one component in an integrated MSW management system. The potential for source reduction, separation, and recycling to increase the safety and efficiency of incineration should be counted among their many benefits. Risk considerations dictate that alternatives to the use of toxic metals at the production stage also be examined in designing an effective, long-term MSW management strategy.     

Flexible Threshold Models for Modelling Interest Rate Volatility

This paper focuses on interest rate models with regime switching and extends previous nonlinear threshold models by relaxing the assumption of a fixed number of regimes. Instead we suggest automatic model determination through Bayesian inference via the reversible jump Markov Chain Monte Carlo (MCMC) algorithm. Moreover, we allow the thresholds in the volatility to be driven not only by the interest rate but also by other economic factors. We illustrate our methodology by applying it to interest rates and other economic factors of the American economy.     

Multitaper testing of spectral lines and the detection of the solar rotation frequency and its harmonics

We develop statistical testing methodology for closely spaced spectral lines based on multitaper spectrum estimates and apply it to a time series of solar magnetic field magnitude data recorded by the Ulysses spacecraft. The test is formulated through complex-valued weighted least squares. Using this test in combination with the test for well-separated lines, we can accurately detect the solar equatorial rotation frequency of the sun, and its harmonics, from data recorded by Ulysses's magnetometer at solar mid-latitudes; this is a potentially important result for solar physicists, since it has implications for the structure and dynamics of magnetic fields in the solar corona.     

Disabled People in a Newly Industrialising Economy: opportunities and challenges in Malaysia

Malaysia is one of the fastest developing countries in the world and by the year 2020 it will become an industrialised nation according to the Malaysian government's strategic plans. This dynamic situation poses numerous opportunities and challenges for disabled people in Malaysia. Two positive outcomes are opportunities in the employment sector as well as infrastructure developments. However, there are new challenges especially with regards to mainstreaming of educational and vocational training along with the need for a dynamic placement service. The rise in industrial accidents creates a new challenge, along with the changing demographic trends which could have a negative impact on disabled people. What is striking is that disabled people and their organisations are beginning to articulate these changes and making a claim for inclusion in Malaysian society.     

Flexible Threshold Models for Modelling Interest Rate Volatility

This paper focuses on interest rate models with regime switching and extends previous nonlinear threshold models by relaxing the assumption of a fixed number of regimes. Instead we suggest automatic model determination through Bayesian inference via the reversible jump Markov Chain Monte Carlo (MCMC) algorithm. Moreover, we allow the thresholds in the volatility to be driven not only by the interest rate but also by other economic factors. We illustrate our methodology by applying it to interest rates and other economic factors of the American economy.