3,032 research outputs found
Modelling Australian Domestic and International Inbound Travel: a Spatial-Temporal Approach
In this paper Australian domestic and international inbound travel are modelled by an anisotropic dynamic spatial lag panel Origin-Destination (OD) travel flow model. Spatial OD travel flow models have traditionally been applied in a single cross-sectional context, where the spatial structure is assumed to have reached its long run equilibrium and temporal dynamics are not explicitly considered. On the other hand, spatial effects are rarely accounted for in traditional tourism demand modelling. We attempt to address this dichotomy between spatial modelling and time series modelling in tourism research by using a spatial-temporal model. In particular, tourism behaviour is modelled as travel flows between regions. Temporal dependencies are accounted for via the inclusion of autoregressive components, while spatial autocorrelations are explicitly accounted for at both the origin and the destination. We allow the strength of spatial autocorrelation to exhibit seasonal variations, and we allow for the possibility of asymmetry between capital-city neighbours and non-capital-city neighbours. Significant spatial dynamics have been uncovered, which lead to some interesting policy implications.Tourism demand, Dynamic panel models, Travel flow model.
Synthesis of Gaussian Trees with Correlation Sign Ambiguity: An Information Theoretic Approach
In latent Gaussian trees the pairwise correlation signs between the variables
are intrinsically unrecoverable. Such information is vital since it completely
determines the direction in which two variables are associated. In this work,
we resort to information theoretical approaches to achieve two fundamental
goals: First, we quantify the amount of information loss due to unrecoverable
sign information. Second, we show the importance of such information in
determining the maximum achievable rate region, in which the observed output
vector can be synthesized, given its probability density function. In
particular, we model the graphical model as a communication channel and propose
a new layered encoding framework to synthesize observed data using upper layer
Gaussian inputs and independent Bernoulli correlation sign inputs from each
layer. We find the achievable rate region for the rate tuples of multi-layer
latent Gaussian messages to synthesize the desired observables.Comment: 14 pages, 9 figures, part of this work is submitted to Allerton 2016
conference, UIUC, IL, US
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