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Computer Science
Algorithms and Data Structures
Probabilistic Programming and Data Structures
1. Foundational Concepts in Probability and Statistics
2. Probabilistic Programming Foundations
3. Inference Algorithms for Probabilistic Programming
4. Probabilistic Programming Languages and Tools
5. Model Development and Validation
6. Probabilistic Data Structures Theory
7. Membership and Set Operations
8. Cardinality Estimation
9. Frequency Estimation and Heavy Hitters
10. Similarity and Distance Estimation
11. Advanced Probabilistic Data Structures
12. Integration and System Design
13. Applications and Case Studies
Cardinality Estimation
Problem Formulation
Count-Distinct Problem
Streaming Constraints
Accuracy Requirements
Linear Counting
Bit Vector Approach
Hash Function Requirements
Estimation Formula
Error Analysis
Memory Requirements
Probabilistic Counting
Flajolet-Martin Algorithm
Bit Pattern Analysis
Geometric Distribution
Variance Reduction
LogLog Counting
Bucket-Based Approach
Leading Zero Counting
Harmonic Mean Estimation
Bias Correction
HyperLogLog
Algorithm Description
Hash Value Processing
Bucket Assignment
Maximum Leading Zeros
Estimation Process
Harmonic Mean Formula
Bias Correction Factors
Small Range Corrections
Error Bounds
Standard Error Analysis
Confidence Intervals
Practical Considerations
Parameter Selection
Memory Usage
Merging Operations
Extensions
HyperLogLog++
Sparse Representation
Compressed Counters
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7. Membership and Set Operations
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9. Frequency Estimation and Heavy Hitters