Taking continuous probability over discrete probability?

In summary, if you are taking the upper-level probability course at your school, you will have covered most of the concepts taught in the elementary probability course. However, if you are just taking the elementary probability course, some of the concepts will be new to you.
  • #1
Eclair_de_XII
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I'm considering taking the upper-level probability course at my school over the elementary course offered because of time constraints. The latter is not a prerequisite for the former. Do you think I will be alright taking the more advanced probability course over the elementary course? Any input offered will be much appreciated. Thank you.

MATH 371 Elementary Probability Theory (3) Sets, discrete sample spaces, problems in combinatorial probability, random variables, mathematical expectations, classical distributions, applications. Pre: [Calculus II]

MATH 471 Probability (3) Probability spaces, random variables, distributions, expectations, moment-generating and characteristic functions, limit theorems. Continuous probability emphasized. Pre: [Calculus IV]

Here are the courses. I've already taken Calculus IV, so I can just skip to 471.
 
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  • #2
Most of the concepts in MATH 371 are baked into MATH 471 because continuous probability theory is built up in a similar way to discrete prob theory. However, if you just took 471, some of the stuff you'd miss out on learning include important probability distributions and combinatorics, which is by the way very important in statistical physics, computer science etc.

That said, I'd put priority on taking both 371 and 471. If you want to do 471, you'll have to learn most of what's in 371 anyway, so might as well do it properly and take both courses. Also, understanding statistics and probability theory is extremely useful for physicists/engineers.
 
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  • #3
My suggestion would be to speak to the instructors for both courses if possible (or the chair of the math department) and get their assessment of which sequence of probability courses are worth taking before taking any action.

That being said, from the limited description above, it does seem like MATH 471 would include most of the concepts taught in MATH 371. So you might be OK with taking just MATH 471, but taking both may be a better idea.
 
  • #4
Hey Eclair_de_XII

For undergraduate courses, most of the concepts are redundant between the two categories of distributions.

For graduate coursework, you learn how to deal with both when you look at measure theory and probability.

You should know this if you want to choose one course over another - and the main difference for undergraduate courses is that you will see Sigma's instead of integrals and different probability transform/generation functions for the discrete and continuous cases.
 
  • #5
Thank you, everyone. I ended up deciding to take 371 and its supplementary course first, and then 471 and that supplementary course one year later. I'm kind of worried that I might forget my Calculus IV by then...
 

1. What is the difference between continuous and discrete probability?

Continuous probability refers to the probability of a continuous random variable taking on a specific value within a given range, while discrete probability refers to the probability of a discrete random variable taking on a specific value. In other words, continuous probability deals with variables that can take on an infinite number of values within a certain range, while discrete probability deals with variables that can only take on a finite number of values.

2. How is continuous probability calculated?

Continuous probability is calculated using integration, which involves finding the area under a probability density function (PDF). The probability of a continuous random variable taking on a specific value is equal to the area under the PDF curve at that point. Unlike discrete probability, there is no specific formula for calculating continuous probability, as it depends on the shape of the PDF.

3. Why is continuous probability important in scientific research?

Continuous probability is important in scientific research because it allows for the analysis of continuous data, such as measurements taken from instruments or physical processes. By using continuous probability, researchers can make more accurate predictions and inferences about real-world phenomena.

4. Can continuous probability be applied to discrete data?

No, continuous probability cannot be applied to discrete data. This is because discrete data can only take on a finite number of values, while continuous probability deals with an infinite number of values. In order to use continuous probability, the data must be continuous and measured on a continuous scale.

5. How does the concept of limits relate to continuous probability?

Limits play a crucial role in continuous probability as they define the boundaries of a continuous random variable. In order to calculate the probability of a continuous random variable taking on a specific value, we must take the limit as the range approaches that specific value. Without limits, the concept of continuous probability would not be possible.

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