An anomaly model learning apparatus for detecting an anomalous state of an industrial apparatus, characterized by comprising: an anomaly distribution acquiring part adapted to obtain, from anomalous data, which is operation sound data obtained in an anomalous state of the industrial apparatus, or from values based on the obtained anomalous data, an anomaly distribution, and to output the anomaly distribution to an anomalous data generating part, wherein the anomaly distribution is a probability distribution that is followed by the anomalous data; the anomalous data generating part adapted to pseudo-generate anomalous data, hereinafter referred to as pseudo-anomalous data, such that a probability that the pseudo-anomalous data has been generated from the anomaly distribution is high, and a probability that the pseudo-anomalous data has been generated from a probability distribution followed by normal data, which is operation sound data obtained in a normal state of the industrial apparatus, is low, and to output the pseudo-anomalous data to a threshold setting part, wherein pieces of the pseudo-anomalous data are sampled from the anomaly distribution; the threshold setting part adapted to set, as a threshold for judging whether data are normal or anomalous, a smaller value than the minimum value of anomaly scores calculated from the pseudo-anomalous data using the anomaly distribution and the probability distribution followed by normal data, and to output the threshold to a model updating part, wherein the threshold is determined by subtracting a predetermined positive constant from a smallest one of the anomaly scores, and wherein the anomaly scores are determined based on the anomaly distribution; and the model updating part adapted to update parameters of the anomaly distribution which is an anomaly model optimized using an objective function, with normal data obtained in a normal state, the obtained anomalous data or the values based on the obtained anomalous data, and the threshold, wherein the objective function is a function defined so that an anomalous data is certainly judged as anomalous and a false positive rate, which is a probability of a normal data being misjudged as anomalous, is minimized. an anomaly distribution acquiring part adapted to obtain, from anomalous data, which is operation sound data obtained in an anomalous state of the industrial apparatus, or from values based on the obtained anomalous data, an anomaly distribution, and to output the anomaly distribution to an anomalous data generating part, wherein the anomaly distribution is a probability distribution that is followed by the anomalous data; the anomalous data generating part adapted to pseudo-generate anomalous data, hereinafter referred to as pseudo-anomalous data, such that a probability that the pseudo-anomalous data has been generated from the anomaly distribution is high, and a probability that the pseudo-anomalous data has been generated from a probability distribution followed by normal data, which is operation sound data obtained in a normal state of the industrial apparatus, is low, and to output the pseudo-anomalous data to a threshold setting part, wherein pieces of the pseudo-anomalous data are sampled from the anomaly distribution; the threshold setting part adapted to set, as a threshold for judging whether data are normal or anomalous, a smaller value than the minimum value of anomaly scores calculated from the pseudo-anomalous data using the anomaly distribution and the probability distribution followed by normal data, and to output the threshold to a model updating part, wherein the threshold is determined by subtracting a predetermined positive constant from a smallest one of the anomaly scores, and wherein the anomaly scores are determined based on the anomaly distribution; and the model updating part adapted to update parameters of the anomaly distribution which is an anomaly model optimized using an objective function, with normal data obtained in a normal state, the obtained anomalous data or the values based on the obtained anomalous data, and the threshold, wherein the objective function is a function defined so that an anomalous data is certainly judged as anomalous and a false positive rate, which is a probability of a normal data being misjudged as anomalous, is minimized.