1 - Oregon State University

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Parameterizing the modelX io iZ iY itd itW itt=1,…,Ti=1,…,Mz i ~P(z i |x i ): Species Distribution ModelP z i = 1 x i = o i = F(x i ) “occupancy probability”y it ~P(y it |z i , w it ): Observation modelP y it = 1 z i , w it = z i d itd it = G(w it ) “detection probability”28TAAI 201111/11/2011
Standard Approach: Log Linear (logisticregression) models log logF X1−F XG W1−G W Train via EM= β 0 + β 1 X 1 + ⋯ + β J X J= α 0 + α 1 W 1 + ⋯ + α K W K People tend to use very simple models: J = 4, K = 229TAAI 201111/11/2011
Page 1 and 2: Graphical Models and FlexibleClassi
Page 3 and 4: Outline Two Cultures of Machine Lea
Page 5 and 6: § 17. Allgemeine Rechtsgrundsätze
Page 7 and 8: Advantages and Disadvantages ofPara
Page 9 and 10: Another Advantage:Interactions and
Page 11 and 12: Can we combine the best of both?Pro
Page 15 and 16: Fitting logistic regression via gra
Page 17 and 18: L2-Tree Boosting Algorithm Let F 0
Page 19 and 20: Tree Boosting for Conditional Rando
Page 21 and 22: Tree Boosting for Latent Variable M
Page 23 and 24: Species Distribution ModelingObserv
Page 25 and 26: Wildlife Surveys with Imperfect Det
Page 27: Occupancy-Detection ModelOccupancyf
Page 31 and 32: Alternating Functional Gradient Des
Page 33 and 34: Features33TAAI 201111/11/2011
Page 35 and 36: Results for AUC of y itNo significa
Page 37 and 38: Partial Dependence PlotSynthetic Sp
Page 39 and 40: PartialDependencePlotBlue Jay vs.Ti
Page 41 and 42: Open Problems Sometimes the OD mode
Page 43 and 44: Next Steps Modeling Expertise in Ci
Page 45 and 46: Occupancy/Detectability/ExpertiseMo
Page 47 and 48: New Project: BirdCast Goal: Contine
Page 51 and 52: Combining Two Approaches toMachine

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