Linear causal model discovery using the MML criterion

Determining the causal structure of a domain is a key task in the area of Data Mining and Knowledge Discovery.The algorithm proposed by Wallace et al. [15] has demonstrated its strong ability in discovering Linear Causal Models from given data sets. However, some experiments showed that this algorithm experienced difficulty in discovering linear relations with small deviation, and it occasionally gives a negative message length, which should not be allowed. In this paper, a more efficient and precise MML encoding scheme is proposed to describe the model structure and the nodes in a Linear Causal Model. The estimation of different parameters is also derived. Empirical results show that the new algorithm outperformed the previous MML-based algorithm in terms of both speed and precision.

An improved approach for the discovery of causal models via MML

Discovering a precise causal structure accurately reflecting the given data is one of the most essential tasks in the area of data mining and machine learning. One of the successful causal discovery approaches is the information-theoretic approach using the Minimum Message Length Principle[19]. This paper presents an improved and further experimental results of the MML discovery algorithm. We introduced a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. The experimental results of the current version of the discovery system show that: (1) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal models with large number of variables; (3) the new version works more efficiently compared with the previous version in terms of time complexity.

The discovery of generalized causal models with mixed variables using MML criterion

One major difficulty frustrating the application of linear causal models is that they are not easily adapted to cope with discrete data. This is unfortunate since most real problems involve both continuous and discrete variables. In this paper, we consider a class of graphical models which allow both continuous and discrete variables, and propose the parameter estimation method and a structure discovery algorithm based on Minimum Message Length and parameter estimation. Experimental results are given to demonstrate the potential for the application of this method.

A discussion on passive location discovery in emitter networks using angle-only measurements

In this paper we discuss the ghost node problem found when triangulation of 2 or more nodes is required. We present and discuss a simple algorithm, termed ABLE (Angle Based Location Estimation), that will position randomly placed emitters in a wireless sensor network using a mobile antenna array. The individual nodes in the network are relieved of the localization task by the mobile antenna system and require no modifications to account for location determination. Furthermore, no beacon nodes (i.e. nodes that know their own position) are required. We provide analysis that indicates a reasonably small number of measurements are required to guarantee the successful
localization of the emitting nodes and demonstrate our results through simulation.

A study on reliability in graph discovery

A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated.

In this paper we will firstly provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be.

If the price is right : vagueness and values clarification in contingent valuation

The use of willingness to pay to value the benefits of health care is increasing. Much of this work assumes that health preferences are well formed or complete and readily revealed if the right question is asked in the right way. We examined this assumption, seeking evidence in a mixed-methods study that explored the meaning and implications of vague responses to a payment-scale based willingness to pay exercise.

One-half of the sample said that their vagueness meant that their maximum willingness to pay was actually greater than the amount that they had previously said it was. Thirty percent agreed that they would probably pay £10 more than a sum that they had previously said they would most definitely not pay, if they found this to be the cost of the vaccine. Interview data supported the view that the payment scale had failed to elicit the maximum willingness to pay and that some participants used the information on cost to help clarify their values, in contrast to the theory underpinning willingness to pay. The results suggest a need to consider values-clarification in health economic evaluations. Copyright © 2002 John Wiley & Sons, Ltd.

Examination of environmental beliefs and its impact on the influence of price, quality and demographic characteristics with respect to green intention

Research indicates that the environment has had a definite impact on consumer behaviour whereby suggesting to target consumers according to their environmental beliefs. This study investigated the consumers' green purchase behaviour using price and quality attributes as contributors to the formation of purchase intention. It attempts to construct a model that may facilitate the better understanding of green consumers' market segments through the use of an intelligent soft computing model. The model is designed to incorporate knowledge, beliefs, demographic profiles and situational variables. This potentially provides a more direct method for companies to gauge consumers' intention to purchase green products. The results showed strong preference for companies to place higher priority on reducing pollution than on increasing profitability. It highlighted different clusters that demonstrate various levels of the strength of intention to purchase and market segment profiles.

Granger causality among house price and macroeconomic variables in Victoria

This study analyses the dynamic causality of four macroeconomic variables on house prices. The four macroeconomic variables have interrelationships with house prices in certain lagged terms, but these relationships are not always the same as the notions put forward in prior research. The relationships are detected to be unstable in the three observation periods. The instability of these relationships would cause difficulty in predicting house prices in the market, especially for policy makers and market participants.

Discovery of late Changhsingian (latest Permian) brachiopod Attenuatella species from South China

Attenuatella mengi sp. nov. and ?Attenuatella sp. from the Talung Formation, southern Guangxi Zhuang Autonomous Region, South China, are described herein. This discovery represents the first report of Attenuatella from the late Changhsingian (latest Permian) in South China and provides evidence that Attenuatella expanded its range from high-latitude cold-water regions to palaeoequatorial warm water areas in the Late Permian. Attenuatella
species appear to have been pseudoplanktonic, judging from their hair-like spinose ornamentation, which could have contributed to the global palaeogeographical distribution of Attenuatella.

Housing price diffusion pattern of Australia's state capital cities

The ripple effect of house prices within metropolitan areas has recently been recognised by researchers. However, it is very difficult to formulate and measure this effect using conventional house price theories particularly in consideration of the spatial locations of cities. Based on econometrics principles of the cointegration test and the error correction model, this research develops an innovative approach to quantitatively examine the diffusion patterns of house prices in mega-cities of a country. Taking Australia's eight capital cities as an example, the proposed approach is validated in terms of an empirical study. The results show that a 1-1-2-4 diffusion pattern exists within these cities. Sydney is on the top tier with Melbourne in the second; Perth and Adelaide are in the third level and the other four cities lie on the bottom. This research may be applied to predict the regional housing market behavior in a country.

Modelling oil price volatility

In this paper, we examine the volatility of crude oil price using daily data for the period 1991–2006. Our main innovation is that we examine volatility in various sub-samples in order to judge the robustness of our results. Our main findings can be summarised as follows: (1) across the various sub-samples, there is inconsistent evidence of asymmetry and persistence of shocks; and (2) over the full sample period, evidence suggests that shocks have permanent effects, and asymmetric effects, on volatility. These findings imply that the behaviour of oil prices tends to change over short periods of time.

Exploring the oil price and real GDP nexus for a small island economy, the Fiji Islands

The goal of this paper is to examine the relationship between real GDP and oil prices using time series data for the period 1970–2005. Our main finding is that an increase in oil has a positive, albeit inelastic, impact on real GDP, inconsistent with the bulk of the literature. We argue that this is not a surprising result for the Fiji Islands. Our central argument focuses on two aspects of the Fijian economy: (1) the fact that actual output in Fiji has been around 50 per cent less than potential output; thus, Fiji's actual output has not reached a threshold level at which oil prices can negatively impact output; and (2) a rise in oil prices filters through to value added, which in turn is reflected in a larger actual output.