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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
Membership and Set Operations
Bloom Filters
Basic Structure and Operations
Bit Array Representation
Multiple Hash Functions
Insertion Algorithm
Query Algorithm
Error Analysis
False Positive Probability
Optimal Parameter Selection
No False Negatives Guarantee
Parameter Optimization
Bit Array Size Selection
Number of Hash Functions
Memory-Error Tradeoffs
Variants and Extensions
Counting Bloom Filters
Counter Arrays
Deletion Support
Counter Overflow Handling
Scalable Bloom Filters
Dynamic Growth
Multiple Filter Levels
Compressed Bloom Filters
Space Optimization
Compression Techniques
Spectral Bloom Filters
Frequency Information
Multi-Set Support
Cuckoo Filters
Cuckoo Hashing Principles
Two Hash Functions
Displacement Strategy
Load Factor Limits
Filter Construction
Fingerprint Generation
Bucket Organization
Insertion Algorithm
Query and Deletion
Membership Testing
Item Removal
False Positive Analysis
Comparison with Bloom Filters
Space Efficiency
Deletion Capability
Performance Characteristics
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6. Probabilistic Data Structures Theory
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8. Cardinality Estimation