Probability Density Function (vs. Histogram vs. Gaussian) I am unsure of the definition and purpose of a PDF.
I have heard it being described as essentially a smoothing of a histogram. I've heard it stated that its main advantage over a histogram is that bin sizes are not a factor anymore in how the distribution looks like.
Are these things essentially true?
In terms of purpose, does it have any other purpose than what the histogram is for?
Finally, I'm confused about the pdf relation to Gaussian/Normal distribution.
The pdf looks kind of like a bell curve and this is kind of confusing. Does a pdf always look like a bell curve?
 A: You are confusing several different concepts.

*

*Probability density function (pdf) is a kind of mathematical function that tells us what is the "probability per foot" for a continuous random variable. Probability density function $f$ has such properties that $f(x) \ge 0$ for all $x$ and $\int\, f(x) \,dx = 1$. We also can use it to calculate probabilities over intervals, $\Pr(a \le x \le b) = \int_a^b \, f(x)\, dx$.

*Probability density functions can have all different shapes, the "bell curve", i.e. Gaussian, known also as normal distribution is just one of the possibilities. To give one counterexample, the uniform random variable has a probability density function that has a shape of a rectangle, there's nothing "bell-curved" about it.

*Histogram is an estimator, it approximates probability density function based on some data.

*What you seem to be describing as density that is a "smoother histogram" is another estimator: kernel density estimator. While histogram learns a binned distribution, kernel density estimator uses a smooth function to approximate the probability density function estimating it from the data. Kernel density estimator is defined in terms of kernels, where one of the popular kernels is a Gaussian function.

