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Statistics
Statistical Computing
1. Introduction to Statistical Computing
2. Programming Fundamentals for Statistics
3. Data Management and Manipulation
4. Numerical Methods for Statistics
5. Simulation and Resampling Methods
6. Advanced Computational Methods
7. Statistical Model Implementation
8. Visualization and Communication
9. Software Engineering for Statistics
Advanced Computational Methods
Markov Chain Monte Carlo
Bayesian Inference Foundations
Bayes' Theorem
Prior Distributions
Likelihood Functions
Posterior Distributions
Conjugate Priors
Markov Chain Theory
Markov Property
Transition Matrices
Stationary Distributions
Ergodicity and Mixing
Detailed Balance
MCMC Algorithms
Metropolis Algorithm
Metropolis-Hastings Algorithm
Proposal Distributions
Acceptance Probability
Tuning Parameters
Gibbs Sampler
Full Conditional Distributions
Blocked Gibbs Sampling
Data Augmentation
Hamiltonian Monte Carlo
Leapfrog Integration
No-U-Turn Sampler
MCMC Implementation
Chain Initialization
Burn-in Period
Thinning
Multiple Chains
MCMC Diagnostics
Convergence Assessment
Trace Plots
Running Means
Gelman-Rubin Statistic
Geweke Diagnostic
Autocorrelation Analysis
Effective Sample Size
Monte Carlo Standard Error
Advanced MCMC Topics
Adaptive MCMC
Parallel Tempering
Reversible Jump MCMC
High-Performance Computing
Performance Analysis
Profiling Code
Time Profiling
Memory Profiling
Line-by-Line Analysis
Identifying Bottlenecks
Benchmarking
Code Optimization
Algorithmic Improvements
Data Structure Optimization
Memory Access Patterns
Compiler Optimizations
Parallel Computing Concepts
Shared Memory vs. Distributed Memory
Task Parallelism vs. Data Parallelism
Synchronization and Communication
Load Balancing
Parallel Programming
Thread-Based Parallelism
OpenMP
Pthreads
Process-Based Parallelism
MPI
MapReduce
Language-Specific Parallel Computing
Parallel Processing in R
Multiprocessing in Python
Distributed Computing in Julia
GPU Computing
GPU Architecture
CUDA Programming
OpenCL
GPU Libraries for Statistics
cuBLAS
cuSolver
GPU-accelerated R packages
Big Data and Scalable Computing
Big Data Characteristics
Volume, Velocity, Variety
Data Storage Challenges
Processing Challenges
Memory-Efficient Computing
Out-of-Core Algorithms
Memory Mapping
Chunked Processing
Sparse Data Structures
Distributed Computing Frameworks
Hadoop Ecosystem
Apache Spark
Dask for Python
Distributed R
Streaming Data Processing
Online Algorithms
Incremental Statistics
Real-Time Analytics
Cloud Computing
Cloud Platforms
Containerization
Serverless Computing
Auto-scaling
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5. Simulation and Resampling Methods
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7. Statistical Model Implementation