13. Probability Density Function (pdf)
Probability Density Functions (PDFs) are essential in the context of continuous random variables. They describe the distribution of values along with their properties, enabling the calculation of probabilities and statistical modeling. Key applications of PDFs span various fields, including engineering and data science, where they help analyze random phenomena effectively.
Enroll to start learning
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- A Probability Density Function (PDF) defines the distribution of continuous random variables.
- The properties of PDFs include non-negativity and the requirement that the total area under the curve equals one.
- Probability is calculated over intervals, and the expected value and variance can be deduced from PDFs.
Key Concepts
- -- Probability Density Function (PDF)
- A function that describes the likelihood of a continuous random variable taking on a particular value.
- -- Cumulative Distribution Function (CDF)
- A function that provides the probability that a random variable is less than or equal to a certain value.
- -- Expected Value
- The average value of a random variable calculated from its probability density function.
- -- Variance
- A measure of the dispersion of a set of values; it indicates how far the values are spread out from the mean.
Additional Learning Materials
Supplementary resources to enhance your learning experience.