Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted as little as possible of the data by applying CS and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the structure a...

A new distance-based estimator for forest regeneration assessment, the mean distance method, was developed by combining ideas and techniques from the wandering quarter method, T-square sampling and the random pairs method. The performance of the mean distance method was compared to conventional 4.05 square meter plot sampling through simulation analysis on 405 square meter blocks of a field sur...

a. Follow-ups to Session 13 • Comment on the codes from Session 13. The codes used in Session 13 use various " tricks " or shortcuts to make them more compact. In general, I don't recommend coding this way (so these codes should be rewritten!). For example, the choice of energy units is exploited so that energies are integers and therefore can be used as array indices. But if we added an extern...

The sampling method has been paid much attention in the field of complex network in general and statistical physics in particular. This paper presents two new sampling methods based on the perspective that a small part of vertices with high node degree can possess the most structure information of a network. The two proposed sampling methods are efficient in sampling the nodes with high degree....

In recent years, online social networks (OSN) have emerged as a platform of sharing variety of information about people, and their interests, activities, events and news from real worlds. Due to the large scale and access limitations (e.g., privacy policies) of online social network services such as Facebook and Twitter, it is difficult to access the whole public network in a limited amount of ...

BACKGROUND Nonrandom sampling of populations in developing nations has limitations and can inaccurately estimate health phenomena, especially among hard-to-reach populations such as rural residents. However, random sampling of rural populations in developing nations can be challenged by incomplete enumeration of the base population. METHODS We describe a stratified random sampling method usin...

Generation of deviates from random graph models with non-trivial edge dependence is an increasingly important problem. Here, we introduce a method which allows perfect sampling from random graph models in exponential family form (“exponential family random graph” models), using a variant of Coupling From The Past. We illustrate the use of the method via an application to the Markov graphs, a fa...