Sentence examples for sparsity probability from inspiring English sources
Under these power constraints, the corresponding scaling parameters αs 1, αs 2 and α r can be derived, where the average power of symbol A1u, A2u and B u are determined by the row weight of corresponding sampling matrix and sparsity probability of u.
Fig. 4 The relationship between reconstruction probability and sparsity of the RIP.
Feature sparsity means the occurrence probability in a certain document of a feature which belonged to the document set is very low.
The simulation results validate that our method produces higher probability of sparsity pattern recovery and better approximation recovery compared to two state-of-the-art Lasso methods.
Fig. 3 The elemental maps of the sub-coherence υ and the block-coherence μ B. Figure 4 is the relationship between reconstruction probability and sparsity of the RIP.
It is apparent that if we use { Φ ( m ) } m = 0 n as the sampling matrices for a given sparsity domain, the failure probabilities for Φ 0) and Φ(n) are, respectively, one and zero, and as the index m increases, the failure probability decreases.
However, by exploiting the sparsity and regularity of transition probabilities, we can use algorithmic shortcuts to reduce the complexity to O(n L).
Note that there are regions where multiple founder ancestries have similar probabilities due to sparsity of markers, low genotyping quality, or similarity of DNA sequence among multiple founders (e.g., due to identity-by-descent); in such cases, crossovers can be localized only to long intervals.
Due to data sparsity, we cannot always efficiently determine probability estimates in language models.
For a different sparsity value k, the curves of probability of exact reconstruction are shown in Fig. 1.
Additionally, large numbers of parameters need to be estimated, and due to the sparsity characteristics of natural language, the probability of low- and zero-frequency events is estimated crudely and inaccurately using various smoothing algorithms.
