Topics

1

General Discussion / Sensitivity to evidence

« on: October 08, 2008, 12:06:19  »

In the evidence sensitivity analysis chapter in the Hugin manual, in Figure 1 a "Sensitivity to evidence" window is shown. How do I obtain this wizard? In the run mode, the "Analysis wizard" that I get is different from the one shown in Figure 1..

2

General Discussion / Sensitivity set

« on: October 08, 2008, 11:13:15  »

Is there a standard option for checking the sensitivity set in Hugin? I understood that this set is different from the set of non-d-separated nodes (which can be checked by d-separation analysis in Hugin).

3

HUGIN Training Course Discussion / distribution parameters

« on: June 08, 2007, 14:03:19  »

Since assessing (posterior) distribution parameters (such as mean and standard deviation) depends very much on the choice of discretization (intervals), I assume that there is no standard procedure (Hugin function call) for doing this.
Thus, assumingly, one has to export the values of the posterior probabilities that are found (these values  can be seen in the graphs in the "Recompile" mode). 
How can I export these values, for example to xls or Mathematica? What is the most convenient way to compute the distribution parameters?

4

HUGIN Training Course Discussion / interval nodes

« on: May 16, 2007, 16:04:15  »

How exactly does Hugin compute with interval nodes? E.g. let A be the parent node of B where A consists of the intervals 0-1 and 1-2, and B consists of the intervals 0-1,1-2,2-3,3-4, and the likelihood relation is B=2*A. We observe probability mass table:
        a1      a2
b1   0.48    0
b2   0.52    0
b3   0         0.48
b4   0         0.52
Why isn't 50% of the probabilty mass of B in [0,1) and 50% in [1,2) for A \in [0,1) and
50% of the probabilty mass of B in [2,3) and 50% in [3,4) for A \in [1,2)?

Can B also be numbered s.t. by an expression the intervals of A get transformed into a number?

5

HUGIN Training Course Discussion / continuous distributions

« on: March 30, 2007, 14:52:05  »

A part of my network consists of 2 continuous parent nodes "mu" and "sigma^2", with continuous child node A, with distribution A~N(mu,sigma^2). The mean of A "mu" is not known, but follows some distribution. Also the variance of A "sigma^2" is not known, but follows some distribution. Two questions:

1. How to set the prior distributions on "mu" and "sigma^2", if they are different from a Normal distribution. (In the table of these nodes I only have to specify the mean and sigma2. This means assumption of a normal distribution?) For example, I want to give "mu" and "sigma^2" a beta-pert-distribution.

2. How to fill in the table for A? Opening the table will me make fill in Mean, mu, sigma^2, Variance. Filling in 1 and 1 for mu resp. sigma^2, does this mean that the parent distributions only influence the distribution of A(Mean,Variance) by means of a "disturbance" (like in the modelling example Temperature.net from the course the quality of the thermometer does)? Thus does it only add mu to Mean and sigma^2 to Variance?

I hope someone can help me.
Joost