Technology such as cloud computing and modern data analysis methods such as Hadoop and Map Reduce are helping to store and handle this large volume of data. However, the storage and security are major concerns. Data redundancy is a common problem in cloud and big data. De-duplication methods ...
In subject area: Computer Science A probabilistic data structure is defined as a structure that allows the insertion of data and a check function, such as Bloom Filter (BF) and Cuckoo Filter (CF), which enable a rate of false positives but not false negatives by using hash functions to map...
Probabilistic aspects in cluster analysisH BockCluster analysis provides methods and algorithms for partitioning a set of objects O = 1,…, n (or data vectors x1,…, xn ∈ R p ) into a suitable number of classes C1,…,Cm O such that these classes are homogeneous and each of them com...
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"Hyperloglog: The analysis of a near-optimal cardinality estimation algorithm". Discrete Mathematics and Theoretical Computer Science Proceedings. MinHash: Andrei Z. Broder, "On the resemblance and containment of documents", in Compression and Complexity of Sequences: Proceedings (1997). Invertible ...
Probabilistic modelsform the foundation for much work inmachine learning, computer vision, signal processing and dataanalysis. The formulation and solution of such models rests on the two simple equations ofprobabilitytheory, the sum rule and the product rule. However, the simplicity of these equatio...
Statistical data analysis The statistical procedure employed to estimate a context tree for each participant and for each electrode was introduced in Duarte et al.19, to which we refer the reader for a complete description of the method including the proofs of the consistency result. This procedu...
This is an implementation of HyperLogLog as described by Flajolet, Fusy, Gandouet, and Meunier in HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm.HyperLogLog is a probabilistic algorithm which approximates the number of distinct elements in a multiset. It works by hashing...
CEN architectures for survival analysis 04 Bayesian Learning of NNs Bayesian learning of NN parameters q Deep kernel learningA neural network as a probabilistic model: Likelihood: p(y|x,θ)p(y|x,θ) Categorical distribution for classification ⇒ cross-entropy loss 交叉熵损失 Gaussian distribution...
The load test database is presented in Section 2, while the load-displacement behaviours and their best regression forms are demonstrated in Section 3. The best-fit marginal distribution of the regression parameters is also identified. A bivariate analysis using copulas is elaborated in Section 4....