Bibliography – a full list of books and papers

General

K. P. Murphy. (2012) Machine Learning: A Probabilistic Perspective. The MIT Press.

A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, D. B. Rubin. (2013) Bayesian Data Analysis. Chapman and Hall/CRC, third edition.

Data collection methods

J.D. Wegner, S. Branson, D. Hall, K. Schindler, and P. Perona. (2016) Cataloging Public Objects Using Aerial and Street-Level Images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [link].

D. Sheldon, A. Farnsworth, and J. Irvine. (2013) Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar. In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI). [link].

J. Salamon, J. P. Bello, A. Farnsworth, and S. Kelling. (2017) Fusing shallow and deep learning for bioacoustic bird species classification. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [link]

Sensor placement

C. Guestrin, A. Krause, and A. Singh. (2005) Near-optimal sensor placements in gaussian processes. Proceedings of the 22nd International conference on Machine Learning (ICML). [link]

Y. Xue, B. Dilkina, T. Damoulas, and D. Fink. (2013) Improving Your Chances: Boosting Citizen Science Discovery. AAAI Conference on Human Computation and Crowdsourcing. [link]

Weakly supervised learning

F. Briggs, B. Lakshminarayanan, L. Neal, X. Fern, R. Raich, S. Frey, A. Hadley, and M. G. Betts. (2012) Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach. Journal of the Acoustical Society of America, vol. 131, no. 6, pp. 4640–4650. [link].

A. T. Pham, R. Raich, X. Z. Fern, and J. P. Arriaga. (2015) Multi-instance multi-label learning in the presence of novel class instances. Proceedings of the 32nd International conference on Machine Learning (ICML). [link].

Occupancy-detection model

D. MacKenzie, J. Nichols, G. Lachman, S. Droege, J. A. Royle, and C. Langtimm. (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology, 83(8), pp.~2248–2255. [link]

R. Hutchinson, L.-P. Liu, and T. Dietterich. (2011) Incorporating Boosted Regression Trees into Ecological Latent Variable Models. AAAI Conference on Artificial Intelligence (AAAI). [link].

Species Distribution Modeling

T. M. Hegel, S. A. Cushman, J. Evans, and F. Huettmann. (2010) Current State of the Art for Statistical Modelling of Species Distributions In: Spatial Complexity, Informatics, and Wildlife Conservation edited by S. A. Cushman and F. Huettmann. Chapter 16, pp.~273–311. [link].

S. Phillips, M. Dudík, and R. Schapire, A maximum entropy approach to species distribution modeling. Proceedings of the twenty-first International Conference on Machine Learning (ICML), pp. 655–662, 2004. [link].

N. Golding, and B. V. Purse. (2016) Fast and flexible Bayesian species distribution modelling using Gaussian processes. Methods in Ecology and Evolution, 7: 598–608. [link]

A. Datta, S. Banerjee, A. O. Finley, and A. E. Gelfand. (2016) Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets. Journal of the American Statistical Association, 111(514). [link].

E. E. Crone. (2016) Contrasting effects of spatial heterogeneity and environmental stochasticity on population dynamics of a perennial wildflower. Journal of Ecology 104: 281-291. [link].

Trajectories modeling

C. B. Schultz, B. G. Pe’er, C. Damiani, L. Brown, and E.E. Crone. (2017) Does movement behaviour predict population densities? A test with 25 butterfly species. Journal of Animal Ecology, 86, pp.~(384–393). [link].

T. A. Patterson, A. Parton, R. Langrock, P. G. Blackwell. L. Thomas, and R. King. (2016) Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges. ArXiv e-prints stat.AP, 1603.07511. [link]

D. Sheldon, T. G. Dietterich. Collective Graphical Models. (2011) Advances in Neural Information Processing Systems 24 (NIPS 2011). [link]

D. R. Brillinger. (2010) Modeling Spatial Trajectories. In: Handbook of Spatial Statistics Edited by A. E. Gelfand, P. Diggle, P. Guttorp, and M. Fuentes. CRC Press. [link].

D. R. Brillinger. (2007) Learning a Potential Function from a Trajectory. In: IEEE Signal Processing Letters, 14(11), 867–870. [link].

Capture recapture modeling

M. Kéry and M. Schaub. (2011) Estimation of the Size of a Closed Population from Capture–Recapture Data. Chapter 6 of Bayesian population analysis using WinBUGS – A hierarchical perspective. Academic Press. [link]

D. L. Borchers and M. G. Efford. (2008) Spatially Explicit Maximum Likelihood Methods for Capture-Recapture Studies. Biometrics, 64(2), pp.~377-385. [link]

A. K. Fuller, C. S. Sutherland, J. A. Royle, M. P. Hare. (2016) Estimating population density and connectivity of American mink using spatial capture–recapture. Ecological Applications, 26(4), pp.~1125–1135. [link]