On being alive now and Bayesian probabilities....

In summary, the OP is questioning if their current existence increases the likelihood of having lived for billions of years and being stuck in a virtual reality. This is not something that can be assessed through Bayesian analysis and is more of a philosophical musing.
  • #1
dessert1
Hi everyone! I am new here, I have been reading you for many years and I just registered hoping that you can help me out with these disturbing thoughts, as they are taking away too much of my time :(

Let's say my lifespan is around 80 years. The lifespan of the universe is of the order of billions or trillions of years. Yet, I am alive. Using bayesian analysis, does the fact that I am alive now increase the likelihood of me actually having been alive for much longer, and that I am (e.g.) for billions of years stuck in a virtual reality living different lives continuously?
 
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  • #2
Yes, the odds of you being alive for billions of years are greatly reduced if, in fact, you are currently dead
 
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  • #3
No. You are alive and conscious. Enjoy your life.
 
  • #4
Yes, you are alive and conscious but are you awake?

Your notion is really taking our understanding of the universe a bit too far, approaching the realm of philosophical musings which we are not equipped to discuss here.

However, you could expand your notions into a science fiction story and have fun where ever it takes you.
 
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  • #5
To the OP:

In all seriousness, you use the terms "Bayesian analysis" freely, but I don't feel that you really understand what Bayesian analysis actually involves.

To do proper Bayesian analysis, you first have to formulate a model that you hope is adequate to describe the situation or phenomenon of interest. Then you formulate a prior distribution over the unknown parameters of the model, which is meant to capture your beliefs (or pre-existing speculations/facts) about the situation before looking at the data. After observing some data, you then apply Bayes Rule to obtain a posterior distribution for these unknowns, taking account of both the prior and the data. You can then use the posterior distribution to compute predictions about future observations.

I honestly don't see at any stage how your speculation about being alive for billions of years being stuck in a virtual reality multiverse can be assessed through the above description of Bayesian analysis. What is the prior -- the fact that you are alive now? What data can you gather?
 
  • #6
Thread closed for Moderation...
 
  • #7
A very speculative post based on the OP's question has been deleted. It is probably not possible to discuss this within the PF rules beyond the replies that are left above, so this thread will remain closed.
 

1. What are Bayesian probabilities?

Bayesian probabilities are a type of statistical inference that updates the probability of a hypothesis as new evidence is collected. It is based on Bayes' rule, which calculates the likelihood of a hypothesis given prior knowledge and new evidence.

2. How are Bayesian probabilities used in science?

Bayesian probabilities are used in a wide range of scientific fields, including physics, medicine, and social sciences. They are particularly useful in situations where there is uncertainty and limited data, as they allow for the incorporation of new information to refine the probability of a hypothesis.

3. What is the difference between Bayesian probabilities and traditional statistics?

The main difference between Bayesian probabilities and traditional statistics is the approach to uncertainty. Traditional statistics uses fixed probabilities, while Bayesian probabilities allow for the updating of probabilities as new evidence is gathered. Additionally, Bayesian probabilities require the use of prior knowledge, while traditional statistics does not.

4. Can Bayesian probabilities be applied to real-world problems?

Yes, Bayesian probabilities have been successfully applied to many real-world problems, including medical diagnosis, financial forecasting, and weather prediction. They are particularly useful in situations where there is limited data and high uncertainty.

5. Are there any limitations to using Bayesian probabilities?

One limitation of Bayesian probabilities is the requirement for prior knowledge, which can be subjective and prone to bias. Additionally, the calculations can become complex and time-consuming, especially with large datasets. However, with advancements in technology, these limitations are becoming less of a barrier.

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