# Bernoulli and Bayesian probabilities

• hdp12
In summary, the conversation discusses a problem on estimating parameters using the maximum likelihood approach and the Bayesian approach. The first few parts of the problem are done correctly, but the final question asks about the accuracy of the two methods. The conversation concludes that it is impossible to determine which method is more accurate without knowing the true value of the population parameter.
hdp12
Summary:: Hello there, I'm a mechanical engineer pursuing my graduate degree and I'm taking a class on machine learning. Coding is a skill of mine, but statistics is not... anyway, I have a homework problem on Bernoulli and Bayesian probabilities. I believe I've done the first few parts correctly, but the final question asks me to explain why one is more accurate than another, and the inverse as well. I am not sure, so I figured I'd reach out here and ask. The work and appropriate equations are below:

1. (10 pts) Consider 20 values randomly sampled from the Bernoulli Distribution with parameter :

Matlab:
x = [1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1];
N = length(x);

(a) Estimate the parameter using the maximum likelihood approach and the 20 data values.
Matlab:
u = sum(x==1)/N; % u = 0.75
bern = (u.^x).*(1-u).^(1-x)

p = 0;
for n = 1:N
pTemp = x(n)*log(u) + (1-x(n))*log(1-u);
p = p+pTemp;
end

%ln(a) = b <--> a = e^b
p = exp(p); % p = 1.3050e-05
(b) Estimate the parameter using the Bayesian approach. Use the beta distribution Beta(a=8, b=4).
Matlab:
% a + sum(xn),b + N - sum(xn)
% (8 + 15 - 1) / (12 + 20 - 2) = 22/30
u = 22/30; % u = 0.7333

(c) Estimate the parameter using the Bayesian approach. Use the beta distribution Beta(a=4, b=8).
Matlab:
% (4 + 15 - 1) / (12 + 20 - 2) = 18/30
u = 18/30; % u = 0.6
(d) Discuss why the estimation from (b) is more accurate than that from (a) and why the estimation from (c) is worse than that from (a).
Matlab:
uA = 0.75;
uB = 0.7333;
uC = 0.6;

Thanks in advance for any help!

I think something goes wrong in the first step (a).
The max likelihood estimate of the Bernoulli parameter is simply the number of 1s divided by the sample size, which gives 15/20 = 0.75.
I don't understand your reason for doing the calcs you show above, which appear to give an answer of approx 10^-5.

It is too hard to work out what you were trying to do based only on computer code. Better to write out mathematical reasoning and explain the steps you took.

Also, we can't assess accuracy without knowing what the population parameter is. It looks like you meant to include that in the problem statement, but it is missing.

hdp12
I did a little too much in part A, you're right.

nothing else was provided in the problem statement though. Is there any reason why one estimation would be more accurate than another method of estimation?

Do you know the true value of your parameter u (e.g. with what value of the parameter your x was generated)? Otherwise I don't understand the question about accuracy ... How would you know if the MLE or the Bayesian posterior is more accurate if you don't know the actual value that you try to predict in this case?

However, if I assume that true value should be somewhere near 0.7, then the comparison between (c)-(a) is rather straightforward if you plot your Beta function prior in the case of (c)...

hdp12 said:
Is there any reason why one estimation would be more accurate than another method of estimation?
Yes. "Accuracy" measures the difference between the estimate and the true population parameter. Your data sample gives a MLE estimate of 0.75. You have done two different Bayesian calcs, call them B1 and B2, producing estimates of 0.7333 and 0.6. If you line these up on a number line, you can see that :
• MLE is most accurate if the population parameter is greater than (0.7333 + 0.75) / 2, approx 0.742
• B1 is most accurate if the population parameter is between (0.6 + 0.7333) / 2 and (0.7333 + 0.75) / 2 (approx between 0.67 and 0.742)
• B2 is most accurate if the population parameter is less than (0.6 + 0.7333) / 2 (approx 0.67).
Any of those three could be true given the information provided in the OP!

As @ChrisVer points out, if the question doesn't state the population parameter's value, it is impossible to answer.

## 1. What is the difference between Bernoulli and Bayesian probabilities?

Bernoulli probabilities are used to calculate the likelihood of a single event occurring, while Bayesian probabilities involve updating prior beliefs based on new evidence or information.

## 2. How are Bernoulli and Bayesian probabilities applied in real-world scenarios?

Bernoulli probabilities are often used in binary situations, such as flipping a coin or determining the success or failure of an experiment. Bayesian probabilities are commonly used in fields such as medicine, finance, and machine learning to make predictions and decisions based on new data.

## 3. Can Bernoulli and Bayesian probabilities be used together?

Yes, Bernoulli probabilities can be used as a building block for Bayesian probabilities. In Bayesian analysis, the prior belief can be represented as a Bernoulli distribution, which is then updated with new data to form a posterior distribution.

## 4. What are the limitations of Bernoulli and Bayesian probabilities?

Bernoulli probabilities assume that each trial is independent and that the probability of success remains constant. Bayesian probabilities can be limited by the choice of prior belief and the amount and quality of data available.

## 5. Are there any real-world examples where Bernoulli and Bayesian probabilities have been applied successfully?

Yes, there are many examples where Bernoulli and Bayesian probabilities have been used successfully, such as in clinical trials for new medications, predicting stock market trends, and spam filtering in email systems.

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