Rolling Markov Chain Monte Carlo
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1 Rolling Markov Chain Monte Carlo Din-Houn Lau Imperial College London Joint work with Axel Gandy 4 th September 2013 RSS Conference 2013: Newcastle
2
3 Output predicted final ranks of the each team. Updates quick update of predictions. Accuracy control of the prediction variance.
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5 Football Model: In-Season Each team has a strength - constant over a season. Match: Home Team strength x H vs Away Team strength x A Home Goals: Poisson(λ H exp (x H x A )) Away Goals: Poisson(λ A exp (x A x H )) λ H and λ A - home and away (dis)advantage.
6 Football Model: Between-Season Denote X t as the vector of team strengths in season t. Between seasons: X t+1 N(βCX t, σ 2 si). C centering matrix, β contraction / expansion, σ 2 s variance of noise between seasons.
7 Football Model: Between-Season Denote X t as the vector of team strengths in season t. Between seasons: X t+1 N(βCX t, σ 2 si). C centering matrix, β contraction / expansion, σ 2 s variance of noise between seasons. Promotion strengths N(µ p, σ 2 pi).
8 X 1 X 2 X 3 Hidden
9 X 1 X 2 X 3... Hidden θ Hidden (λ H, λ A, β, σ s, µ p, σ p )
10 Y 1 Y 2 Y 3,1 Y 3,2 Y 3,3 Observed X 1 X 2 X 3... Hidden θ Hidden (λ H, λ A, β, σ s, µ p, σ p )
11 Y 1 Y 2 Y 3,1 Y 3,2 Y 3,3 Observed X 1 X 2 X 3... Hidden θ Hidden (λ H, λ A, β, σ s, µ p, σ p ) Hidden Markov Model + no closed form for p(θ, x 1:T y 1:T ) + prior = Markov Chain Monte Carlo methods.
12 RMCMC Control Deletion Reweight Sample Database
13 Initialisation RMCMC Control Deletion Reweight Initial Data Sample Database
14 Initialisation RMCMC on GOOD Accuracy Control Indicator BAD RMCMC Control Deletion Reweight Initial Data Sample Database
15 Initialisation RMCMC on GOOD Accuracy Control Indicator BAD RMCMC Control Deletion Reweight Initial Data Sample Database
16 Initialisation RMCMC paused GOOD Accuracy Control Indicator BAD RMCMC Control Deletion Reweight Initial Data Sample Database
17 Initialisation RMCMC paused GOOD Accuracy Control Indicator BAD RMCMC Control Deletion Reweight Initial Data Sample Database
18 New Data Observed Control Indicator Accuracy GOOD BAD RMCMC Control Deletion Reweight New Data Sample Database
19 New Data Observed Reweight on GOOD Accuracy Control Indicator BAD GOOD Quality BAD RMCMC Control Deletion Reweight New Data Sample Database
20 New Data Observed Decrease Database Size GOOD Accuracy Control Indicator BAD GOOD Quality BAD RMCMC Control Deletion Reweight New Data Sample Database
21 New Data Observed Un-pause RMCMC GOOD Accuracy Control Indicator BAD GOOD Quality BAD RMCMC Control Deletion Reweight New Data Sample Database
22 GOOD increase database size Quality pause RMCMC RMCMC on BAD decrease database size GOOD Accuracy BAD
23 Simulation Initial Data: results from 2005/06 to 2009/10 English Premier League seasons. Batches: reveal data in weekly batches for the 2010/11 and 2011/12 seasons. Typically 10 results per batch, although between 3 and 20 results. Results
24 Conclusion Summary: Use this to control the accuracy of the predictions as new data are observed. Efficient RMCMC turns on when necessary. Deletes samples when possible. Not a low dimensional state space. MCMC step can be replaced by other methods such as Particle MCMC (Andrieu et al., 2010) and SMC 2 (Chopin et al., 2013).
25 References Football Website: co.uk/users/fdl06/plwebsite/ Andrieu, C., Doucet, A., and Holenstein, R. (2010). Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3): Chopin, N., Jacob, P. E., and Papaspiliopoulos, O. (2013). Smc2: an efficient algorithm for sequential analysis of state space models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(3): Lau, F. D.-H. and Gandy, A. (2013). Rolling Markov Chain Monte Carlo - A System for Sequential Updating. ArXiv e-prints.
Rolling Markov Chain Monte Carlo
Rolling Markov Chain Monte Carlo Din-Houn Lau Imperial College London Joint work with Axel Gandy 4 th July 2013 Predict final ranks of the each team. Updates quick update of predictions. Accuracy control
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