Dynamic prediction of interface level using spatial temporal markov random field

Liu, Zheyuan; Kodamana, Hariprasad; Afacan, Artin; Huang, Biao

doi:10.1016/j.compchemeng.2019.05.035

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…This is known as a Gibbs distribution. Time-dynamic (or time-variying) Markov Random Fields (TD-MRFs) are the plain extension of MRFs for dynamic scenarios (see [19]). These are a special sort of dynamic graphical models (DGMs) which explicitly model the correlations in space and in time as dependencies amongst the random variables such that the corresponding joint distribution function can be written by means of functions of the corresponding time-varying random variable over the cliques C ∈ C:…”

Section: Dynamic Graphical Models Time-dynamic Markov Random Fieldsmentioning

confidence: 99%

Time-Dynamic Markov Random Fields for price outcome prediction in the presence of lobbying

Cabello

2021

Appl Intell

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This paper presents a mathematical/Artificial Intelligence (AI) model for the prediction of price outcomes in markets with the presence of lobbying, whose outputs are pricing trends that aggregate the opinions of lobbies on future prices. Our proposal succeeds in unraveling this complex real-world problem by reducing the solution to straightforward probability computations. We tested our method on real olive oil prices (Andalusia, Spain) with encouraging results in a challenging sector, where opacity in the entry of oil shipments which are stored while waiting for the price to rise, makes it very difficult to forecast the prices. Specifically, understanding by minimum price that the price level is at least reached, specific formulas for computing the likelihood of both the aggregate and the minimum market price are provided. These formulas are based on the price levels that lobbies expect which in turn, can be calculated using the probability that each lobby gives to market prices. An innovative quantitative study of the lobbies is also carried out by explicitly computing the weight of each lobby in the process thus solving a problem for which there were only qualitative references up until now. The structural model is based on Time Dynamic Markov random fields (TD-MRFs). This model requires significantly less information to produce an output and enjoys transparency during the process when compared with other approaches, such as neural networks (known as black boxes). Transparency also ensures that the internal structures can be fine tuned to fit to each context as well as possible.

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Section: Dynamic Graphical Models Time-dynamic Markov Random Fieldsmentioning

confidence: 99%

Time-Dynamic Markov Random Fields for price outcome prediction in the presence of lobbying

Cabello

2021

Appl Intell

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Spatial spillover effect of carbon emission efficiency in the construction industry of China

Deng

Zhou

et al. 2021

Environ Sci Pollut Res

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The construction industry plays an important role in energy saving and carbon emissions mitigation of China. Promoting carbon emission efficiency is seen as an efficient way to abate carbon emissions. Using 2005-2016 data, the carbon emission efficiency of the construction sector in 30 provinces is estimated, and the spatial distribution characteristics of the carbon emission efficiency of the construction industry is explored. The spatial Markov transition probability matrix is employed to investigate the influence of the spatial spillover effect on the regional distribution pattern of carbon emission efficiency. The results demonstrate that the carbon emission efficiency of the construction industry exhibits an unbalanced regional distribution, which is high in the east and low in the west. The spatial autocorrelation indicates that the carbon emission efficiency has a spatial dependence and is characterized by spatial agglomeration. Markov Chain results show a significant spatial spillover effect in carbon emission efficiency. The provinces with higher carbon emission efficiency have a positive effect on their neighbors, while the provinces with lower efficiency have a negative effect on neighbors. The findings are of great importance to understand the differences in and interactions of carbon emission efficiency between regions.

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Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking

2021

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Dynamic prediction of interface level using spatial temporal markov random field

Cited by 7 publications

References 28 publications

Time-Dynamic Markov Random Fields for price outcome prediction in the presence of lobbying

Time-Dynamic Markov Random Fields for price outcome prediction in the presence of lobbying

Spatial spillover effect of carbon emission efficiency in the construction industry of China

Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking

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