Generalized Autoregressive Moving Average Models (GARMA) google
A class of generalized autoregressive moving average (GARMA) models is developed that extends the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. The dependent variable is assumed to have a conditional exponential family distribution given the past history of the process. The model estimation is carried out using an iteratively reweighted least squares algorithm. Properties of the model, including stationarity and marginal moments, are either derived explicitly or investigated using Monte Carlo simulation. The relationship of the GARMA model to other models is shown, including the autoregressive models of Zeger and Qaqish, the moving average models of Li, and the reparameterized generalized autoregressive conditional heteroscedastic GARCH model (providing the formula for its fourth marginal moment not previously derived). The model is demonstrated by the application of the GARMA model with a negative binomial conditional distribution to a well-known time series dataset of poliomyelitis counts. …

Machine Vision (MV) google
Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance in industry. The scope of MV is broad. MV is related to, though distinct from, computer vision. …

GraphH google
It is common for real-world applications to analyze big graphs using distributed graph processing systems. Popular in-memory systems require an enormous amount of resources to handle big graphs. While several out-of-core systems have been proposed recently for processing big graphs using secondary storage, the high disk I/O overhead could significantly reduce performance. In this paper, we propose GraphH to enable high- performance big graph analytics in small clusters. Specifically, we design a two-stage graph partition scheme to evenly divide the input graph into partitions, and propose a GAB (Gather-Apply- Broadcast) computation model to make each worker process a partition in memory at a time. We use an edge cache mechanism to reduce the disk I/O overhead, and design a hybrid strategy to improve the communication performance. GraphH can efficiently process big graphs in small clusters or even a single commodity server. Extensive evaluations have shown that GraphH could be up to 7.8x faster compared to popular in-memory systems, such as Pregel+ and PowerGraph when processing generic graphs, and more than 100x faster than recently proposed out-of-core systems, such as GraphD and Chaos when processing big graphs. …

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