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packtpublishing
GitHub Repository: packtpublishing/machine-learning-for-algorithmic-trading-second-edition
Path: tree/master/figures/Chapter_10
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Figure 10.1 - How evidence updates the prior to the posterior probability distribution.png38.1 KB
Figure 10.10 - Forest and energy plot.png118.6 KB
Figure 10.11 - Single-variable model predictions.png124.7 KB
Figure 10.12 - The Bayesian SR in plate notation.png33.3 KB
Figure 10.13 - The posterior distribution for the model parameters.png306.7 KB
Figure 10.14 - The difference between two Bayesian SRs in plate notation.png297.2 KB
Figure 10.15 - The posterior distributions for the differences between two Bayesian SRs.png265.1 KB
Figure 10.16 - Price series and correlation over time of two pairs of trading candidates.png72.8 KB
Figure 10.17 - Changes in intercept and slope coefficients over time.png262.3 KB
Figure 10.18 - Rolling regression lines and price series.png333.5 KB
Figure 10.19 - Daily S&P 500 log returns.png173.1 KB
Figure 10.2 - Posterior distributions of the probability that the S&P 500 goes up the next day after up to 500 updates.png219.3 KB
Figure 10.20 - Trace plot for the stochastic volatility model.png576.1 KB
Figure 10.21 - Model.png369.2 KB
Figure 10.3 - Mutual information between recession and leading indicators for horizons from 1-24 months.png31 KB
Figure 10.4 - Leading indicator distributions by recession status.png53.3 KB
Figure 10.5 - Bayesian logistic regression.png19.7 KB
Figure 10.6 - Traces after 100 samples.png162.4 KB
Figure 10.7 - Traces after an additional 50,000 samples.png177.5 KB
Figure 10.8 - Credible intervals for yield curve and leverage.png28.9 KB
Figure 10.9 - Traces after 400 and after over 200,000 samples.png439.9 KB