The article introduces a new method for estimating probability densities when the exact shape is known in advance. Traditional methods can give poor results, but this new approach, called maximum penalized likelihood estimation, offers more stable and accurate estimates. By using a mathematical concept called reproducing kernel Hilbert spaces, the method produces nonparametric estimates known as polynomial splines. This means that the new method can provide better estimates of probability densities in situations where traditional methods struggle.