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CDF vs PDF In simple terms, the PDF represents the shape of the distribution, while the CDF represents the accumulation of probabilities as the value of the random variable increases. Learn more about Probability Distribution: Definition & Calculations.

In probability theory, a probability density function (PDF) is used to define the random variable's probability coming within a distinct range of values, as opposed to taking on any one value.

Probability Density Function (PDF) vs Cumulative Distribution Function (CDF) The CDF is the probability that random variable values less than or equal to x whereas the PDF is a probability that a random variable, say X, will take a value exactly equal to x.

Understanding Probability Density Functions (PDFs) PDFs are typically depicted on a graph, with a normal bell curve indicating neutral market risk, and a skewed curve at either end indicating greater or lesser risk-reward. Skewness is a shift of the taller portion of the curve to the right or left.

Relationship between PDF and CDF for a Continuous Random Variable By definition, the cdf is found by integrating the pdf: F(x)=x???f(t)dt. By the Fundamental Theorem of Calculus, the pdf can be found by differentiating the cdf: f(x)=ddx[F(x)]

What are the key differences between PDF and CDF? PDF is the probability that a random variable will take a value exactly equal to the random variable, whereas CDF is the probability that a random variable will take a value less than or equal to the random variable.

A PDF is simply the derivative of a CDF. Thus a PDF is also a function of a random variable, x, and its magnitude will be some indication of the relative likelihood of measuring a particular value. As it is the slope of a CDF, a PDF must always be positive; there are no negative odds for any event.