Shouldn't the probability of sampling a point from a continuous distribution be 0?Hey I was reading about Gaussian EM algorithm in which you first calculate the likelihood of data points being sampled from a Gaussian and then adjust your mean and variance to maximize it. To calculate the probability of a point being sampled from a distribution we do P ( x i ∣ θ ) = 1 2 π σ 2 exp ⁡ ( − ( x i − μ ) 2 2 σ 2 ) But how can you sample a point from a continuous distribution? Shouldn't this be zero? Moreover I read many times about sampling a point/data from some continuous distribution but I can't understand how could you do that as for continuous random variable X the P ( X = x 1 ) = 0, so how could yo sample data point(s) from a continuous distribution? Or does this sampling have some other meaning. I've seen many questions on this platform but I couldn't get my answer.

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Shouldn&#039;t the probability of sampling a point from a continuous distribution be 0?Hey I was reading about Gaussian EM algorithm in which you first calculate the likelihood of data points being sampled from a Gaussian and then adjust your mean and variance to maximize it. To calculate the probability of a point being sampled from a distribution we do  P  (      x    i    ∣  θ  )  =      1          2      π              σ        2              exp  ⁡      (                  −        (                  x          i                −        μ                  )          2                            2                  σ          2                      )  But how can you sample a point from a continuous distribution? Shouldn&#039;t this be zero? Moreover I read many times about sampling a point/data from some continuous distribution but I can&#039;t understand how could you do that as for continuous random variable X the   P  (  X  =      x    1    )  =  0, so how could yo sample data point(s) from a continuous distribution? Or does this sampling have some other meaning. I&#039;ve seen many questions on this platform but I couldn&#039;t get my answer.

Karissa Macdonald · Accepted Answer

Decide on the level of accuracy you are working to. For example, if you are modelling masses, you might have the masses being measured to the nearest 5g. That means a measured value of say 45g would in fact be a value between 42.5g and 47.5g. You can then work out the probability that a value is between 42.5g and 47.5g.

Shouldn't the probability of sampling a point from a continuous distribution be 0? Hey I was readin

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