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# Hash function
1. http://fastcompression.blogspot.com/2019/03/presenting-xxh3.html
1. http://cyan4973.github.io/xxHash/
# Reference
1. [Morton Filters: Faster, Space-Efficient Cuckoo Filters via Biasing, Compression, and Decoupled Logical Sparsity](http://www.vldb.org/pvldb/vol11/p1041-breslow.pdf)
1. https://github.com/efficient/SuRF
1. https://medium.com/orbs-network/constructing-bloom-filters-without-false-positives-7aaf50b92f3b
1. https://welcometotheoryland.wordpress.com/2017/10/19/what-is-a-hash-function-it-depends-on-whos-speaking/
1. http://docs.pipelinedb.com/probabilistic.html
1. [Probabilistic Data Structures](https://www.youtube.com/watch?v=F7EhDBfsTA8)
1. http://www.i-programmer.info/programming/theory/4641-the-invertible-bloom-filter.html
1. https://github.com/seiflotfy/hyperbitbit
1. https://blog.yld.io/2017/04/19/hyperloglog-a-probabilistic-data-structure/#.WPtT_R_6zCI
1. http://www.partow.net/programming/bloomfilter/idx.html
1. https://github.com/pawandubey/cuckoo_filter
1. https://cloud.google.com/blog/big-data/2017/07/counting-uniques-faster-in-bigquery-with-hyperloglog
1. https://blog.dataweave.com/using-probabilistic-data-structures-to-build-real-time-monitoring-dashboards-25b17c968c08
1. https://bdupras.github.io/filter-tutorial/
1. https://sagi.io/2017/07/bloom-filters-for-the-perplexed/
1. https://www.somethingsimilar.com/2012/05/21/the-opposite-of-a-bloom-filter/
1. https://github.com/splatlab/cqf
1. [LogLog-Beta and More: A New Algorithm for Cardinality Estimation Based on LogLog Counting](https://arxiv.org/ftp/arxiv/papers/1612/1612.02284.pdf)
1. https://blog.acolyer.org/2017/08/08/a-general-purpose-counting-filter-making-every-bit-count/
1. [Ode on a Random Urn (Functional Pearl)](https://www.youtube.com/watch?v=O37FMxLxm78)
1. http://smalldatum.blogspot.com/2018/09/bloom-filter-and-cuckoo-filter.html
1. https://hackernoon.com/cuckoo-filter-vs-bloom-filter-from-a-gophers-perspective-94d5e6c53299
1. https://github.com/axiomhq/hyperminhash
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