© 2021 Elsevier Inc.Human pose estimation is an important problem which has found applications in different fields. One problem in this context is to estimate the human joints or keypoints under occlusion. Most of the methods in the literature use CNN structures to estimate keypoint positions. In this paper, a Bayesian Approach for occluded Keypoint Estimation, BAKE, is presented to complete the missing human pose elements. A state-of-the-art CNN-based pose estimation algorithm Openpose  is used for detecting the visible joints. Then a Bayesian pose estimation is applied for the missing 2D pose elements. A Gaussian model composed of the length and angle parameters of a 13-keypoint skeleton model to encode the 2D human body pose is presented. Model parameters are obtained from the global and local statistics. Global statistics are extracted from COCO database  whereas the local statistics are obtained from the non-occluded initial video frames under consideration. The local and global statistics are combined and updated by using the action-specific body relations. A new predictability score is proposed to determine the confidence of completing the missing human joints. This confidence score is used to develop a hybrid technique which combines the predictions of the Openpose and the proposed method, BAKE. The proposed approach can be seen as an alternative for a 3D CNN structure which is trying to model the time-dependent sequence of events. Several experiments are performed to compare the outputs of the Openpose, BAKE, and the hybrid approach. It is shown that BAKE outperforms the Openpose estimates in general and the hybrid method generates a slight improvement over the BAKE algorithm.