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MCMC Lottery Prediction
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What Is MCMC?
MCMC stands for Markov Chain Monte Carlo, a powerful simulation method used in data science to explore probabilities of future outcomes. It works by walking through possible states (lottery numbers), updating beliefs based on historical transitions. This technique is widely used in fields like machine learning, Bayesian statistics, and financial modeling. Here, it helps predict numbers with the highest likelihood based on draw history and weighted patterns.
Advanced Settings Explained
This section allows you to fine-tune the behavior of the MCMC algorithm. Even if you're not familiar with MCMC, don’t worry — we’ll walk you through each setting with simple explanations.
Understanding MCMC Settings
Each of the five settings below controls how the Markov Chain Monte Carlo algorithm explores past lottery draws and produces predictions. Adjusting them changes both the speed of computation and the stability (variance vs. bias) of results. Use the “Fast” configuration for quick tests, and the “Thorough” configuration when you need the most reliable output.
1. Simulations (Number of MCMC Walks)
Range: 100 to 40 000
Effect: Number of independent random walks.
More walks → more samples → lower variance in the final visit-frequency estimates; fewer walks → faster but noisier.
2. Burn-In (Discard Initial Steps)
Range: 0 to 2 000
Effect: Discards the first steps of each walk so scoring focuses on the steady-state portion.
Larger burn-in → safer mixing; smaller → faster but may keep transient bias.
3. Laplace K (Smoothing Constant)
Range: 0.1 (or 0) to 10+
Effect: Adds a small count to every transition to avoid zero probabilities.
Higher K → heavier smoothing (flatter differences); lower K → sharper peaks but risk of overfitting.
4. Recency Decay (Weight Recent Draws More)
Range: 0.001 to 1.0
Effect: Controls how quickly older draws lose influence.
Higher decay → strong emphasis on the newest draws; lower decay → more uniform influence over history.
5. Chain Length (Steps per Walk)
Range: 100 to 10 000
Effect: Steps taken per walk after burn-in.
Longer chains → deeper exploration and better mixing; shorter → faster but shallower.
Quick Comparison Table
| Setting | Minimum (Fast) | Maximum (Thorough) | Impact |
|---|---|---|---|
| Simulations (Walks) | 100 – very fast, unstable | 40 000 – very slow, stable | More walks → lower sampling variance. |
| Burn-In | 0 – keep all early steps | 2 000 – discard more transients | Higher burn-in → safer steady-state scoring. |
| Laplace K | 0.1 – minimal smoothing | 10 – heavy smoothing | Avoids zero probabilities; higher K flattens peaks. |
| Recency Decay | 0.001 – near-uniform | 1.0 – very recent favored | Higher decay → stronger bias to latest draws. |
| Chain Length | 100 – short walk | 10 000 – long walk | Longer → deeper exploration; slower per walk. |
Tip: A balanced starting point is
Walks = 1 500, Burn-In = 50, K = 1.5, Decay = 0.02, Chain Length = 1 500.
Simulations (Number of MCMC Walks)
This tells the system how many different “random paths” to simulate using your lottery history. More paths = more stable predictions, but more work.
Burn-In (Discard Initial Steps)
Removes the first few steps so we only use the more stable part of each walk.
Laplace K (Smoothing Constant)
Gives every possible transition a small base count to avoid zero-probability issues.
Recency Decay (Weight Recent Draws More)
Controls how much more influence newer draws have versus older ones.
Chain Length (Steps per Walk)
How many transitions to make in each random walk. More steps = deeper exploration, but slower.
Optimizer (Exhaustive Grid Search)
Grid Search Optimizer (Experimental)
Try every combination of settings in the ranges below to find the best-performing configuration based on historical draw results.
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W: –
B: –
CL: –
K: –
D: –
Live Leaderboard (Top 5 Combos)
Score Heatmap (ChainLen vs Decay — max across K,B)
Color = performance (preview or refined).
Bluer = lower score ? Purple = medium ? Redder = higher score
Outlined squares = refined. ? badge = newly updated.
Progress Log
Backtesting & Insights
Walk-Forward Backtest
Evaluate historical performance using your current model logic. This does not change predictions or saved data.
Backtest Summary
Run Prediction
Use the settings above to run a full MCMC simulation and generate predicted numbers based on statistical transition behavior from historical draws.
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