What is StatSim?
StatSim is a free probabilistic simulation web application designed for statistical modeling and Bayesian inference directly in the browser. It offers a visual programming interface where users can build models using blocks, ranging from simple calculators to complex environmental simulations. The tool supports stochastic modeling with over 20 probability distributions, allowing for the creation of diverse models and analysis through histograms and summary statistics.
Users can perform Bayesian inference using methods like Variational Inference and Markov Chain Monte Carlo to estimate posterior distributions of model parameters. Additional features include constraint solving with a built-in SMT solver, the ability to compile models into system binaries for speed improvements, and various analysis tools such as charts and descriptive statistics. The platform also includes specialized modules for tasks like data analysis, machine learning fitting, time series forecasting, and data visualization, making it a comprehensive suite for statistical work.
Features
- Visual Programming: Create and share programs directly in the browser using blocks for models ranging from simple to complex
- Stochastic Modeling: Utilize 24 probability distributions to construct diverse stochastic models and analyze results with histograms and summary statistics
- Bayesian Inference: Estimate posterior distributions of model parameters using Variational Inference and Markov Chain Monte Carlo methods
- Constraint Solving: Develop constraint-based models and estimate or optimize unknown parameters using a built-in SMT solver
- Compilation: Compile models into system binaries using servers for use as regular programs with additional stats and speed improvements
Use Cases
- Creating probabilistic models for environmental simulations
- Performing Bayesian inference to estimate hidden variables in statistical data
- Analyzing and processing data using online algorithms for real-time insights
- Fitting machine learning models and making predictions based on statistical methods
- Forecasting univariate time series with ARIMA models for trend analysis
- Generating synthetic data for benchmarking and exploration purposes
- Profiling datasets using streaming statistical algorithms to understand data characteristics
- Solving linear programming problems without writing code for optimization tasks
- Visualizing and exploring high-dimensional data in latent space for data science projects
