bayesian

  1. U

    Bayes Nordics - a list for the Bayesian community in the European Nordic countries

    Bayes Nordics is a list serving the Bayesian community in the European Nordic countries. Bayes Nordics disseminates news on events related to Bayesian analysis: workshops, conferences, seminars, job openings and courses related to Bayesian reasoning, methods, practice and computation, with...
  2. hlsmith

    Bayesian Posterior: SD = SE?

    I was curious if the standard deviation in a Bayesian posterior is equal to the standard error?
  3. T

    What's wrong with my BANOVA (Bayesian ANOVA)?

    Hi everyone... I'm new to Bayesian statistics and I'm trying to perform a BANOVA. I'm using the following book to help me: "Doing Bayesian Data Analysis A Tutorial Introduction with R and BUGS", written by John K. Kruschke. I'm working on a simulated data set, trying to find a difference in...
  4. L

    Proof of Kalman filter related lema

    Hi... Can you please provide a proof of the formula given on the attached image?
  5. S

    Imdb top 250 and bottom 100 calculation

    https://en.wikipedia.org/wiki/IMDb#Ranking The above page describes how the top 250 is calculated where the m (prior) value is set at 25000 for top 250, and for bottom 100 , the m value is set at 1500. My question is the following 1)Why conduct bottom 100 separately because the bottom...
  6. T

    decision under loss function, discrete Bayes

    Hi guys, This is my first post here. I am currently enrolled in the Coursera Bayesian Statistics course from Duke University. While I've enjoyed the Statistics with R specialization so far, I think this course is not at the same level as the other ones. In one of the quizzes from the course...
  7. E

    AMOS Bayesian MCMC

    When using Bayesian MCMC in AMOS, do the MCMC algorithms allow for nonlinearity? I know all the other estimation algorithms do not [e.g. maximum-likelihood (ML)]. Also, does anyone know of any research articles that involve Bayesian CFA and measurement invariance that is similar or the same...
  8. I

    Comparing the output of JAGS to conjugate analysis - normal with unknown mean and var

    Dear Bayesians. I'm starting my way in the Bayesian world, and I'm trying to build a simple model for estimating the mean and variance of a normal distribution. I assume that: y=rnorm(100,50,4) # This would be the data mu0=0 #Prior of mean var0=100 #Prior for the variance #I continue...
  9. O

    Evaluation of Bayesian Model

    How can I evaluate if a created bayesian model is reliable model that makes good predication? To conclude if the created bayesian model produces a low error rate.
  10. N

    Finding Joint Posterior Density of Poisson and Exp?

    Hi, Some friends and I have been stuck on this bayesian stats question for five days now, please shed some light if you can :wave: We've tried to multiply the the Poisson posterior density and exponential posterior density together to produce a joint density, but we're told it's not correct :/...
  11. S

    Combining two data sets using Bayesian methods

    Hi, I want to combine multiple data sets of customer spend data together. I want to calculate how the bounds of the data decrease as I add in more data sets (so I can do things like estimate the ROI error for a whole year, as opposed to one week's spend). I believe there is a Bayesian method...
  12. B

    bayesian statistics

    I have a large amount of data, given totals for every hour over a year period for the following columns: # of Jams Jam event time in minutes # of Full occurances Full event time in minutes and Runtime, product flow (counts) I am trying to create a bayesian model using iPython and...
  13. M

    Bayesian MCMC Fitting lognorm mixture model w/constraint on population mean

    I am trying to find a reference and/or guidance for fitting a mixture model to a population of data within an MCMC framwork where the global mean is constrained to a particular value, in this case 1. That is, E(X) = 1 where f(X) = \sum \omega_i f_i(x| \theta_i) and the standard constraint \sum...
  14. N

    Bayesian inference with unequal sampling

    I have a "two-column" data set, with a multi-class categorical variable A, and two-class variable B. It is assumed that each observation is independent. For each category of variable A, I want to make a Bayesian estimate of a binomial parameter for class 1 of variable B, consistent with the...
  15. J

    Bayesian statistics proof question

    I know that if the prior distribution is chosen to be a continuous uniform distribution, then the exact posterior distribution will simply the normalized version of the likelihood function. I was wondering how i would write this out as a proof?
  16. P

    The Future of Clinical Trials Biostatistics - Bayesian?

    I have noticed a trend with hiring managers to vault candidates that are well versed in Bayesian analysis to the top. This is not necessarily because someone will sink or swim based on their allegiance to Bayes, these managers may not even have implemented any of the methodology at all yet...
  17. S

    Bayesian AB Testing - Minimum Sample Size

    I've used python to analyse data from AB tests using Bayesian analysis, and for all tests I assume no prior knowledge and so set alpha = beta = 1. However I'm finding some odd results at low data volumes, which I thought was my code, but I'm also seeing here...
  18. E

    Bayesian regression

    it's a actually an informatics math related question. But i want to start here to get a first opinion. As a software engineer and cryptographer, I'm developing an application to predict to price of certain alt-coin. I have read a paper about the math behind the algorithm. I have a more then...
  19. S

    Multiple Inter-Correlated Binomial Events with only One Event Possible for Success

    Hi you’ll, I have a thought problem that might not have an easy/correct answer. It looks to be a Bayesian at first glance – and it might be – but because I am using some complicated machine-learning algorithms, and because of the nature of the problem, the water are muddy for me. I am just...
  20. A

    Difference between VCV by inverting Hessian at ML and by Hierarchical Bayes

    Let's say we have a multimomial logit problem and we find the best beta coeffcients b* aggregating all units. The inverse of the Hessian at b* gives us a VCV matrix at this point which shows roughly how betas vary across units. The other way to get a VCV matrix is to use Hierarchical Bayes where...