Alvarez, Mauricio, David Luengo, and Neil D Lawrence. 2009. “Latent Force Models.” In Artificial Intelligence and Statistics, 9–16. PMLR.

Amasyali, Kadir, and Nora M El-Gohary. 2018. “A Review of Data-Driven Building Energy Consumption Prediction Studies.” Renewable and Sustainable Energy Reviews 81: 1192–1205.

Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein. 2010. “Particle Markov Chain Monte Carlo Methods.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72 (3): 269–342.

Araya, Daniel B, Katarina Grolinger, Hany F ElYamany, Miriam AM Capretz, and Girma Bitsuamlak. 2017. “An Ensemble Learning Framework for Anomaly Detection in Building Energy Consumption.” Energy and Buildings 144: 191–206.

Bacher, Peder, and Henrik Madsen. 2011. “Identifying Suitable Models for the Heat Dynamics of Buildings.” Energy and Buildings 43 (7): 1511–22.

Beck, James V, Ben Blackwell, and Charles R St Clair Jr. 1985. Inverse Heat Conduction: Ill-Posed Problems. James Beck.

Bellman, Ror, and Karl Johan Åström. 1970. “On Structural Identifiability.” Mathematical Biosciences 7 (3-4): 329–39.

Betancourt, Michael. 2017. “A Conceptual Introduction to Hamiltonian Monte Carlo.” arXiv Preprint arXiv:1701.02434.

Blom, Henk AP, and Yaakov Bar-Shalom. 1988. “The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients.” IEEE Transactions on Automatic Control 33 (8): 780–83.

Candanedo, Luis M, Véronique Feldheim, and Dominique Deramaix. 2017. “A Methodology Based on Hidden Markov Models for Occupancy Detection and a Case Study in a Low Energy Residential Building.” Energy and Buildings 148: 327–41.

Cappé, Olivier, Simon J Godsill, and Eric Moulines. 2007. “An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo.” Proceedings of the IEEE 95 (5): 899–924.

Carstens, Herman, Xiaohua Xia, and Sarma Yadavalli. 2018. “Bayesian Energy Measurement and Verification Analysis.” Energies 11 (2): 380.

Chen, Zhenghua, Chaoyang Jiang, and Lihua Xie. 2018. “Building Occupancy Estimation and Detection: A Review.” Energy and Buildings 169: 260–70.

Chong, Adrian, Godfried Augenbroe, and Da Yan. 2021. “Occupancy Data at Different Spatial Resolutions: Building Energy Performance and Model Calibration.” Applied Energy 286: 116492.

Chong, Adrian, Khee Poh Lam, Matteo Pozzi, and Junjing Yang. 2017. “Bayesian Calibration of Building Energy Models with Large Datasets.” Energy and Buildings 154: 343–55.

Chong, Adrian, and Kathrin Menberg. 2018. “Guidelines for the Bayesian Calibration of Building Energy Models.” Energy and Buildings 174: 527–47.

Chopin, Nicolas, Pierre E Jacob, and Omiros Papaspiliopoulos. 2013. “SMC2: An Efficient Algorithm for Sequential Analysis of State Space Models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 75 (3): 397–426.

Deb, C, and A Schlueter. 2021. “Review of Data-Driven Energy Modelling Techniques for Building Retrofit.” Renewable and Sustainable Energy Reviews 144: 110990.

Deconinck, An-Heleen, and Staf Roels. 2017. “Is Stochastic Grey-Box Modelling Suited for Physical Properties Estimation of Building Components from on-Site Measurements?” Journal of Building Physics 40 (5): 444–71.

Doucet, Arnaud, Nando de Freitas, Kevin Murphy, and Stuart Russell. 2000. “Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks.” In Uncertainty in Artificial Intelligence (Uai), 176–83. San Francisco, CA.

Frigola, Roger. 2015. “Bayesian Time Series Learning with Gaussian Processes.” PhD thesis, University of Cambridge.

Gelman, Andrew, John B Carlin, Hal S Stern, David B Dunson, Aki Vehtari, and Donald B Rubin. 2013. Bayesian Data Analysis. CRC press.

Gelman, Andrew, Jessica Hwang, and Aki Vehtari. 2014. “Understanding Predictive Information Criteria for Bayesian Models.” Statistics and Computing 24 (6): 997–1016.

Ghosh, Siddhartha, Steve Reece, Alex Rogers, Stephen Roberts, Areej Malibari, and Nicholas R Jennings. 2015. “Modeling the Thermal Dynamics of Buildings: A Latent-Force-Model-Based Approach.” ACM Transactions on Intelligent Systems and Technology (TIST) 6 (1): 1–27.

Goffart, Jeanne, and Monika Woloszyn. 2021. “EASI Rbd-Fast: An Efficient Method of Global Sensitivity Analysis for Present and Future Challenges in Building Performance Simulation.” Journal of Building Engineering 43: 103–29.

Granderson, Jessica, Guanjing Lin, Ari Harding, Piljae Im, and Yan Chen. 2020. “Building Fault Detection Data to Aid Diagnostic Algorithm Creation and Performance Testing.” Scientific Data 7 (1): 1–14.

Gray, Francesco Massa, and Michael Schmidt. 2018. “A Hybrid Approach to Thermal Building Modelling Using a Combination of Gaussian Processes and Grey-Box Models.” Energy and Buildings 165: 56–63.

Hartikainen, Jouni, and Simo Särkkä. 2010. “Kalman Filtering and Smoothing Solutions to Temporal Gaussian Process Regression Models.” In 2010 Ieee International Workshop on Machine Learning for Signal Processing, 379–84. IEEE.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.

Heo, Yeonsook, Ruchi Choudhary, and GA Augenbroe. 2012. “Calibration of Building Energy Models for Retrofit Analysis Under Uncertainty.” Energy and Buildings 47: 550–60.

Iooss, Bertrand, and Paul Lemaître. 2015. “A Review on Global Sensitivity Analysis Methods.” In Uncertainty Management in Simulation-Optimization of Complex Systems, 101–22. Springer.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Vol. 112. Springer.

JCGM. 2008. “Evaluation of Measurement Data—Guide to the Expression of Uncertainty in Measurement.” Int. Organ. Stand. Geneva ISBN 50: 134.

Juricic, Sarah. 2020. “Identifiability of the Thermal Performance of a Building Envelope from Poorly Informative Data.” PhD thesis, Université Savoie Mont Blanc.

Kantas, Nikolas, Arnaud Doucet, Sumeetpal S Singh, Jan Maciejowski, Nicolas Chopin, and others. 2015. “On Particle Methods for Parameter Estimation in State-Space Models.” Statistical Science 30 (3): 328–51.

Kennedy, Marc C, and Anthony O’Hagan. 2001. “Bayesian Calibration of Computer Models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63 (3): 425–64.

Kristensen, Martin Heine, Ruchi Choudhary, and Steffen Petersen. 2017. “Bayesian Calibration of Building Energy Models: Comparison of Predictive Accuracy Using Metered Utility Data of Different Temporal Resolution.” Energy Procedia 122: 277–82.

Lundström, Lukas, and Jan Akander. 2020. “Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings.” Energies 13 (1): 76.

Madsen, Henrik. 2007. Time Series Analysis. CRC Press.

Madsen, Henrik, Peder Bacher, Geert Bauwens, An-Heleen Deconinck, Glenn Reynders, Staf Roels, Eline Himpe, and Guillaume Lethé. 2015. “Thermal Performance Characterization Using Time Series Data-Iea Ebc Annex 58 Guidelines.”

Madsen, Henrik, and Jan Holst. 1995. “Estimation of Continuous-Time Models for the Heat Dynamics of a Building.” Energy and Buildings 22 (1): 67–79.

Maillet, Denis. 2010. Problèmes Inverses En Diffusion Thermique. Ed. Techniques Ingénieur.

Murphy, Kevin Patrick. 2002. “Dynamic Bayesian Networks: Representation, Inference and Learning.”

Rasmussen, Carl Edward. 2003. “Gaussian Processes in Machine Learning.” In, 63–71. Springer.

Raue, A., C. Kreutz, T. Maiwald, J. Bachmann, M. Schilling, U. Klingmüller, and J. Timmer. 2009. “Structural and Practical Identifiability Analysis of Partially Observed Dynamical Models by Exploiting the Profile Likelihood.” Bioinformatics 25 (15): 1923–9.

Reddy, T Agami. 2006. “Literature Review on Calibration of Building Energy Simulation Programs: Uses, Problems, Procedures, Uncertainty, and Tools.” ASHRAE Transactions 112: 226.

Rouchier, Simon. 2018. “Solving Inverse Problems in Building Physics: An Overview of Guidelines for a Careful and Optimal Use of Data.” Energy and Buildings 166: 178–95.

Rouchier, Simon, Maria José Jiménez, and Sergio Castaño. 2019. “Sequential Monte Carlo for on-Line Parameter Estimation of a Lumped Building Energy Model.” Energy and Buildings 187: 86–94.

Rouchier, Simon, Mickaël Rabouille, and Pierre Oberlé. 2018. “Calibration of Simplified Building Energy Models for Parameter Estimation and Forecasting: Stochastic Versus Deterministic Modelling.” Building and Environment 134: 181–90.

Särkkä, Simo. 2013. Bayesian Filtering and Smoothing. Cambridge University Press.

Särkkä, Simo, Mauricio A Álvarez, and Neil D Lawrence. 2018. “Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems.” IEEE Transactions on Automatic Control 64 (7): 2953–60.

Särkkä, Simo, and Arno Solin. 2019. Applied Stochastic Differential Equations. Vol. 10. Cambridge University Press.

Shonder, John A, and Piljae Im. 2012. “Bayesian Analysis of Savings from Retrofit Projects.” ASHRAE Transactions 118: 367.

Shumway, Robert H, and David S Stoffer. 2000. Time Series Analysis and Its Applications. Vol. 3. Springer.

Solin, Arno, and others. 2016. “Stochastic Differential Equation Methods for Spatio-Temporal Gaussian Process Regression.”

Vehtari, Aki, Andrew Gelman, and Jonah Bagry. 2016. “Practical Bayesian Model Evaluation Using Leave-One-Out Cross-Validation and Waic.” Statistics and Computing 27 (August): 1413–32.

Vehtari, Aki, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner. 2021. “Rank-Normalization, Folding, and Localization: An Improved R for Assessing Convergence of Mcmc.” Bayesian Analysis 1 (1): 1–28.

Watanabe, Sumio, and Manfred Opper. 2010. “Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory.” Journal of Machine Learning Research 11 (12).

Xie, Yang, and Bradley P Carlin. 2006. “Measures of Bayesian Learning and Identifiability in Hierarchical Models.” Journal of Statistical Planning and Inference 136 (10): 3458–77.

Zoeter, Onno, and T Heskes. 2011. “Expectation Propagation and Generalised Ep Methods for Inference in Switching Linear Dynamical Systems.”