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Distribution Textbook (Work in Progress)
by John Della Rosa
Metropolis Algorithm Visualization
Proposal Std Dev (
σ
):
Number of Iterations:
Starting Point (
x
0
):
Burn-in Period:
Thinning Interval:
Run Metropolis Algorithm
Acceptance Rate: 49.74%
−5
−4
−3
−2
−1
0
1
2
3
4
5
0
0.1
0.2
0.3
0.4
Histogram of Sampled Values
x
Probability Density
plotly-logomark
500
1000
1500
2000
2500
3000
3500
4000
4500
−2
−1
0
1
2
3
Trace Plot of Samples Over Iterations
Iteration
Sample Value
plotly-logomark
10
20
30
40
50
60
70
80
90
100
−0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Full Data
Thinned Data
Autocorrelation Function
Lag
Autocorrelation
plotly-logomark
−4
−3
−2
−1
0
1
2
3
4
−4
−2
0
2
4
QQ Plot of Thinned Samples vs. Standard Normal
Theoretical Quantiles
Sample Quantiles
plotly-logomark