Anomaly detection is an important technique for remotely sensed hyperspectral data exploitation. In the last decades, several algorithms have been developed for detecting anomalies in hyperspectral images. The Reed-Xiaoli detector (RXD) is one of the most widely used approaches for this purpose. Since the RXD assumes that the distribution of the background is Gaussian, it generally suffers from a high false alarm rate. In order to address this issue, we introduce an unsupervised probabilistic anomaly detector (PAD) based on estimating the difference between the probabilities of the anomalies and the background. The proposed PAD takes advantage of the results provided by the RXD to estimate statistical information for the targets and background, respectively, and then uses an automatic strategy to find the most suitable threshold for the separation of targets from the background. The proposed technique is validated using a synthetic data set and two real hyperspectral data sets with ground-truth information. Our experimental results indicate that the proposed method achieves good detection ratios with adequate computational complexity as compared with other widely used anomaly detectors.