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The problem of real-time estimation of occupancy in a commercial building (number of people in various zones at every time instant) is relevant to a number of emerging applications, such as green buildings that achieve high energy efficiency through feedback control. Due to the high deployment cost and large errors that people counting sensors suffer from, measuring occupancy throughout the building accurately from sensors alone is not feasible. Fusing sensor data with model predictions is essential. Due to the highly uncertain nature of occupancy dynamics, modeling and estimation of occupancy is a challenging problem. This paper makes two contributions toward addressing these challenges. We develop an agent-based model to simulate the behavior of all the occupants of a building, and extract reduced-order graphical models from Monte-Carlo simulations of the agent-based model. The agent-based model is validated with sensor data for the special case of one room and one occupant. Noisy measurements from a few sensors are fused with the graphical model predictions using the classical LMV estimator to estimate room-level occupancy in the building. Simulations illustrate the effectiveness of the proposed method.