Likelihood Density Function (PDF)

What Is a Probability Density Function (PDF)?

Likelihood density function (PDF) is a statistical articulation that characterizes a probability distribution (the probability of an outcome) for a discrete random variable (e.g., a stock or ETF) rather than a continuous random variable.

The difference between a discrete random variable is that you can distinguish a precise value of the variable. For example, the value for the variable, e.g., a stock price, just goes two decimal points past the decimal (e.g., 52.55), while a continuous variable could have an endless number of values (e.g., 52.5572389658… ).

At the point when the PDF is graphically depicted, the area under the curve will demonstrate the interval wherein the variable will fall. The total area in this interval of the graph equals the likelihood of a discrete random variable happening. All the more exactly, since the absolute probability of a continuous random variable taking on a specific value is zero due to the boundless set of potential values accessible, the value of a PDF can be utilized to decide the probability of a random variable falling inside a specific scope of values.

The Basics of Probability Density Functions (PDFs)

PDFs are utilized to measure the risk of a specific security, like an individual stock or ETF. They are regularly portrayed on a graph, with a normal bell curve showing neutral market risk, and a bell at either end demonstrating greater or lesser risk/reward. A bell at the right half of the curve recommends greater reward, yet with lesser probability, while a bell on the left shows lower risk and lower reward.

Investors ought to involve PDFs as one of many apparatuses to compute the overall risk/reward in play in their portfolios.

An Example of a Probability Density Function (PDF)

As indicated beforehand, PDFs are a visual device portrayed on a graph in light of historical data. A neutral PDF is the most common visualization, where risk is equivalent to reward across a range.

Somebody ready to face limited challenge may be hoping to anticipate a limited return and would fall on the left half of the bell curve below. An investor ready to face higher challenge searching for higher rewards would be on the right half of the bell curve. The majority of us, searching for average returns and average risk would be at the center of the bell curve.

Features

• PDFs are plotted on a graph regularly looking like a bell curve, with the likelihood of the outcomes lying below the curve.
• PDFs can be utilized to measure the likely risk/reward of a specific security or fund in a portfolio.
• A discrete variable can be measured precisely, while a continuous variable can have boundless values.
• Likelihood Density Functions are a statistical measure used to check the possible outcome of a discrete value (e.g., the price of a stock or ETF).