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Questions tagged [prior]

In Bayesian statistics a prior distribution formalizes information or knowledge (often subjective), available before a sample is seen, in the form of a probability distribution. A distribution with large spread is used when little is known about the parameter(s), while a more narrow prior distribution represents a greater degree of information.

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Non-informative prior for the covariance matrix

I'm currently working on a project around the Bayesian approach to portfolio selection, and I can't manage to wrap my mind around the specification of the non-informative (diffuse) prior. Assuming ...
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Using gaussian as prior probability distribution

I have seen in several places that people use a Gaussian distribution as the prior probability distribution. But when I am assigning probabilities, does using Gaussian a good idea, given that we can ...
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1answer
34 views

Generating data from the posterior distribution

Let $$p(D \mid \mu,\sigma^2) \sim \mathcal{N}(\mu,\sigma^2)$$ where $D=(x_1\ldots x_n)$ is my data. I imposed a normal prior on the mean as $$\pi(\mu) \sim \mathcal{N}(\mu_0,\sigma_0^2)$$ Using Bayes, ...
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dirichlet distribution and excessively large numerator

what I am trying to do is calculating posterior probability using dirichlet distribution as my prior. the situation is like this. a web log have three variables A, B, C, and each variable's value is ...
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Is assigning an inverse-Wishart distribution to a diagonal matrix problematic?

I'm reading the paper Bayesian Vector Autoregressions by Thomas Wozniak. He considers the model $$y_t = \mu + A_1 y_{t-1} + \cdots A_k y_{t-k} + u_t$$ where each $y_i$ is a $N$-vector, each $A_j$ is a ...
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Calibrating LASSO prior (how to select the scale hyperparameter)?

I want to use a LASSO prior (Laplace prior) for a location parameter $\mu$ $$\pi(\mu \mid s) = \dfrac{1}{2s}\exp\left(-\frac{\vert \mu \vert}{s}\right).$$ However, I do not know to calibrate this ...
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42 views

Finding the posterior distribution of a Bayesian analysis prior

I have a prior distribution $f(x)=\pi cos(\pi x) $ where $x$ is the probability of getting tails in a coin toss. Should a coin toss result in tails, how would this be reflected in the posterior ...
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Finding the posterior distribution for Beta likelihood with unknown alpha [duplicate]

If $(y_i|\theta)$ is distributed as $Beta(y_i,\theta,\beta)$ then what prior distribution do I use? My initial thought was to use a Beta prior. Is this right? I found the likelihood but I'm not sure ...
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Posterior as prior for correlated parameters [closed]

I want to use the posterior distribution of the model parameters $\theta$ given data in the time frame $[0,t]$ days, $P(\theta|y_{0:t})$; as a prior for the parameters in the time frame $[t+1, t+n]$ ...
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Significance of parameterisation invariance of Jeffreys prior

I often hear it said that the Jeffreys prior is well-motivated because it is invariant under reparametrization. The proof of this is quite straight-forward (I know the proof on e.g., wiki). I'm a bit ...
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Explanation of the Posterior Derivation of the Gaussian Distribution

I'm reading through my notes and I don't quite understand this bit: I understand how the likelihood was calculated but no more than that.Can anyone explain the steps and exactly how they go from one ...
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Formal Bayesian justification of conditional modelling

I'm having some trouble following the logic of this passage from Chapter 14 in Bayesian Data Analysis, A. Gelman: The numerical 'data' in a regression problem includes both $X$ and $y$. Thus, a ...
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Independence test with priors

Say you are selling a product, and you know from experience that the green version of the product sells better than the blue version. But you have two types of customer A and B, and you want to know ...
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1answer
28 views

Constraints on choice of marginal distribution and likelihood

For some time I have been reading into Bishop's Pattern Recognition and Machine Learning. Coming back to some earlier chapters the following got me confused and I am interested where, formally I go ...
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1D Bayesian Inference clarification

I'd like some help making sure I understand a 1D Bayesian inference problem. Say I have a data vector which is an array of the number of flu cases reported weekly in California for the past 10 years. ...
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1answer
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Crew selection: ranking rowers by letting them race against each other

Seat selection is a common practice in competitive rowing and I would be curious about more solid statistical underpinnings: there are more rowers in a team than the 8 seats in the crew boat. So the ...
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1answer
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How do Bayesian hierarchical models adaptively learn the prior?

It seems the main difference between a hierarchical and a non hierarchical model is that the hierarchical model learns the prior. That is it adaptively comes up with a regularizing prior to be applied ...
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Automatic selection of plot bounds for Normal pdfs in a combined chart

Suppose an app's user specifies several Normal priors by setting the mean and variance for each. I'd like to display a combined plot with the prior pdfs, like that on the figure. . Q: What is a good ...
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How to set a Bayesian prior on a set with a large but unknown number of elements?

Let us suppose that we are trying to analyze a given starfish. We would like to know which species does the starfish belong to. We have a list of 1000 starfish species, but we know that there is an ...
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26 views

Inconsistency of Bayesian time varying VAR model

I'm estimating time-varying parameter VAR model of Joushi Nakajima (2011), the model simulates the time-varying parameters using MCMC algorithm and the priors are estimated by implementing standard ...
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1answer
47 views

Compute conjugate prior from the sample distribution

I feel like this question might be marked as duplicate because I see many similar incurring in that fate but I'll try anyway. I would say I did not find anything similar. I have been thought a ...
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22 views

What is the posterior distribution of a Bernoulli prior that gets updated with a continuous uniform signal?

I'm trying to figure out what the distribution of the posterior is after I update a Bernoulli prior with a continuous uniform signal, say: P(D=G|u)=x where D{G,I} and u is uniformly distributed on ...
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How does L2 penalize large weights

The L2 regularization term is useful because it penalizes large weights over smaller weights which is good to prevent overfitting. I'm having a hard time understanding how exactly it does this. This ...
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In a Hierarchical Bayesian Model, how can we sample and see how a prior distribution looks like if it contains hyperparameters with hyperpriors?

I have a Bayesian Hierarchical Model that looks like: \begin{equation} Y_i \sim N(\mu, \sigma^2) \\ \mu \sim N(\mu_0, \sigma_0^2) \\ \sigma^2 \sim Gamma(1,1) \\ \mu_0 \sim N(0,1) \\ \sigma_0^2 \sim ...
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1answer
26 views

Valid highly informative prior for proportion

I am trying to find a prior distribution for a proportion $\theta$ that is highly informative i.e. it is almost point mass at $\theta$ but I am not able to find such distribution that is valid for a ...
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In a Multi-level Bayesian Hierarchical Model, would higher level parameters be affected by how they are jointly modeled in lower levels?

Suppose we have a Multi-level Hierarchical Model where: $$ \begin{equation} Y_{0i} \sim Bin(\theta_{0i}, n_{0i}) \\ Y_{1i} \sim Bin(\theta_{1i}, n_{1i}) \\ \theta_{0i} \sim Unif(0,1) \\ log\left(\...
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1answer
23 views

Plot Scaling Problem

I am trying to implement the following code in R $\theta \sim Beta(a=1,b=1)$ $x|\theta \sim Bin(N=5,\theta)$ $\theta|x \sim Beta(a+\sum x_{i},b+\sum N-\sum x_{i})$ ...
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How many missings are too many to impute data with the AMELIA II package?

I have a very large longitudinal dataset. I only want to impute 3 variables individually using the AMELIA package in R. The problem is that some individuals have a lot of missing values, so I want to ...
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1answer
49 views

Understanding the Bayesian question [closed]

I have a Bayesian question here: In a drug experiment, patients with a chronic condition are asked to choose between two drugs, C (control), and T (new treatment). (You may assume for the purpose ...
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1answer
87 views

Learning prior distribution from data

Suppose I have a dataset. How can I learn the prior distributions of the parameters of a model from this data? I want to learn the prior from this data in order to use them in a Bayesian model. Sorry ...
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Is this Bayesian model averaging?

A classical example of Bayesian model averaging (BMA) is the regression setup where the choice of different sets of covariates corresponds to different models $\mathcal{M}_k$, $k = 1, \ldots, K$, ...
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Finding mode of posterior using Newton method in R

I am attempting to approximate the posterior $\tilde{\pi_{G}}(z|\theta,Y)$ which is the Gaussian approximation to the full conditional of $z$, and in order to do this I need to find the mode $z^{*} \...
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1answer
23 views

Best estimate of underdetermined system using prior

I have measured two variables which depend on the same set of four parameters. I want to know the parameters which best explain my measurements. Of course, I cannot solve for four unknowns from just ...
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1answer
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Calculating Jeffreys Prior for geometric distribution

This question is already answered here, but I would like to know why it is worked out the way it is My lecture notes state the following: I am also given the following problem : Now, what I ...
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1answer
39 views

Posterior density for a linear regression model

Given a classical linear regression model $$y = X\beta + \varepsilon,$$ $$\varepsilon\sim N(0,\sigma^2I_n),$$ the posterior density is proportional to the product of the likelihood and the selected ...
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Some help interpreting posterior plots

I needed some help interpreting and comparing the plots that I created. I'm not really sure what is the most important thing to talk about when comparing these plots. I know for these plots that the ...
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1answer
26 views

Parameter Transformation Bayesian Learning

I read that given a parameter $\theta$ and a transformation $\phi = g(\theta)$ (where $\theta$ is the parameter of your prior distribution), the distribution of the transformed parameter would be: $...
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1answer
59 views

Elicit a Proper informative priors

I need help with eliciting,deriving and plotting two proper informative prior. One with mean and standard deviation Another with LQ, UQ and Median Is the first one using a normal distribution while ...
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1answer
38 views

Are there situations where improper priors can be avoided via a prior on a subset of the real line and a transformation?

There are many situations where improper priors are "permissable" (Berger, 2009). In many cases, these improper priors are improper because they are "flat" on the real line. A well known example is ...
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How can the prior distribution of bayes regression be estimated by empirical bayes?

Neither in Efron's book Large-scale Inference:Empirical Bayes Methods for Estimation, Testing and Prediction nor by Internet search, did I find a prior distribution estimation method of Bayes ...
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does Informativeness of the prior always decreases similarity of the posterior mean to the data mean? [closed]

I am looking for a proof of the statement "If the variance of the prior distribution is greater, the posterior is more affected by the data". More specifically, if X, X' are priors such that E(X)=E(X'...
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1answer
102 views

Understanding definition of informative and uninformative prior distribution

When using the "non-informative" prior $\pi(\mu,\sigma)\propto\frac{1}{\sigma^2}$ where $\pi(\mu)\propto1$ and $\pi(\sigma^2)\propto\frac{1}{\sigma^2}$ Where is the no information for the ...
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0answers
18 views

What should you consider while constructing a Gaussian prior for the binned fit? [closed]

I need to make Bayesian fit of the binned data. The model is simply linear function (background) + Gaussian (signal). I want to set a Gaussian prior on the mean of the Gaussian from the model. What ...
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54 views

Can the r-scale value (for Bayes Factor) be directly based on Cohen's d?

I want to set an r-scale value for a Bayesian t-test (i.e. to calculate Bayes Factor likelihood ratio) based on previous results (i.e., from a posterior). But I simply cannot find a straightforward ...
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Coming up with decent priors for Bayesian Online Changepoint Algorithm

I am trying to identify changepoints in time series data using this online changepoint detection algorithm. It seems that there are a couple of user defined parameters that I have to give the model: ...
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71 views

Information about parameters using priors distributions [duplicate]

When using the "non-informative" prior $\pi(\mu,\sigma)\propto\frac{1}{\sigma^2}$ where $\pi(\mu)\propto1$ and $\pi(\sigma^2)\propto\frac{1}{\sigma^2}$ Where is the no information for the ...
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1answer
47 views

What would be an ignorance prior of AB, given the probabilities of A and B?

Let us have two events, $A$ and $B$ whose probabilities are $P(A)$ and $P(B)$. In the absence of any other information, what would be a reasonable probability to assign to $AB$, that is, $A$ and $B$ ...
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54 views

Suggested noninformative hyperprior distributions?

I have a hierarchical model that includes a normal distribution and a beta distribution. For the normal distribution, it has two parameters: $\mu$ and $\tau^2$. However, I want to implement ...
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1answer
23 views

What are the limitations on the flat prior in the BAT framework?

By default, the BAT framework sets the flat priors on the parameter. My question is: how in this case allowed range for a parameter of the flat function is estimated?
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19 views

How to Change prior probabilities for predicted variable in neural networks and other methods in SPSS Statistics

i am trying to find right model for predicting categorical variable with two values. Problem is that ratio of cases in group 1 and group 2 is not equal but rather in ratio of 2:1. When i try to find a ...