The way of thinking about depressive symptoms using exponential distribution

So far, we have used stochastic differential equations to explain the process that leads to feelings and thoughts that assume a Wiener process. In reality, however, depressive symptoms may suddenly appear when one is feeling at ease. When a person is suffering from depression, the value of a group of curves, such as the person's similarity curve, is at a negative minimum if we consider the Wiener process. However, a person may go from being at ease to being depressed, i.e., the value of the similarity curve or other curve group may suddenly go from a positive value to a negative minimum. This cannot be explained by the conventional concept of curve group such as similarity curve using the Wiener process. Therefore, we considered depressive symptoms using an exponential distribution. We obtained an equation relating the number of times a person becomes depressed during a certain period of time and the time he or she is depressed, and described the importance of considering the time when a person becomes depressed. Next, we derived an equation giving the time of being depressed and showed that the time of being depressed has an exponential distribution. We also described the connection to similarity curve and other results in the previously discussed paper. Finally, we stated that placing oneself in an environment or state where one is less likely to become depressed is a way to cope with depression.