All Questions
Tagged with abc or approximate-bayesian-computation
16 questions
6
votes
1
answer
337
views
ABC. How can it avoid the likelihood function?
According to the Wikipedia article, we have the scenario shown below, but how can ABC generate simulation datasets from samples of $\theta$ without knowing or evaluating the likelihood function?
For ...
- 19.7k
22
votes
4
answers
8k
views
Likelihood-free inference - what does it mean?
Recently I have become aware of 'likelihood-free' methods being bandied about in literature. However I am not clear on what it means for an inference or optimization method to be likelihood-free.
In ...
- 967
5
votes
2
answers
430
views
Parameter Estimation for intractable Likelihoods / Alternatives to approximate Bayesian computation
Suppose that I have a stochastic model with some parameters that I want to fit to some observed data. Let's assume the Likelihood intractable, i.e. for some reason I cannot work with the analytical ...
- 173
22
votes
2
answers
4k
views
What would be an example of a really simple model with an intractable likelihood?
Approximate Bayesian computation is a really cool technique for fitting basically any stochastic model, intended for models where the likelihood is intractable (say, you can sample from the model if ...
- 6,880
7
votes
1
answer
617
views
In what situations would one use Approximate Bayesian Computation instead of Bayesian inference?
I'm not sure why one would use ABC/Likelihood-free inference methods instead of standard Bayesian inference methods. Is this fundamentally a conceptual problem of mine?
Are there any concrete ...
- 703
4
votes
1
answer
2k
views
Combining multiple posterior distributions
I am new to Bayesian statistics, and thus have problems to come up with a solution for the following problem:
Using Approximate Bayesian Computation (ABC), I generate a posterior distribution from ...
- 143
7
votes
1
answer
293
views
Does Approximate Bayesian Computation (ABC) follow the Likelihood Principle?
I know that ABC is commonly used when the likelihood is intractable, so likelihood principle is not an interest in that case. But, I am curious whether the ABC satisfies the likelihood principle when ...
- 111
6
votes
2
answers
270
views
ABC: Why not use the distance measure as a pseudo-likelihood instead?
I've read about the ABC rejection algorithm when not being able to calculate the likelihood directly, and my question is: if we have to introduce a distance measure $\rho(D,D')$ anyways, why not use ...
- 163
5
votes
1
answer
413
views
ABC with Lotka-Volterra (or any dynamical system)
I have set out to implement a simple ABC rejection sampling algorithm in order to approximate the posterior distribution of parameters for Lotka-Volterra system and I have a few questions:
1) What ...
- 259
4
votes
3
answers
646
views
Approximate Bayesian computation: where to start from? [duplicate]
I am about to start a project in ABC methods and I need to first of all study ABC since I know nothing about it. I spent quite a bit looking for on-line tutorials and notes but I found nothing apart ...
- 729
3
votes
3
answers
1k
views
Understanding the Bayesian Grid Approximation Probabilities
I am working through the survivability analysis shown here. My specific question is regarding the grid approximation method, implemented in the following R code:
<...
- 199
3
votes
1
answer
105
views
Handling a big number of Summary Statistics in ABC
I went through a big amount of literature in $ABC$, in how it is possible to handle a large number of (many cases sufficient) summary statistics. Like a large number, I consider $K>>200$, where $...
- 2,286
2
votes
2
answers
1k
views
Bayesian Statistics -Prior and Posterior distributions
Please is it ever possible for the prior distribution to contain more information about parameter(s) than the posterior distribution? If yes, when can that occur? Is it the same concept as the ...
- 21
2
votes
1
answer
300
views
ABC, compute Bayes factor from posteriors
I am pretty new to ABC stuff so I may be saying dumb things.
My question is: I ran an ABC with two models $M_1$ and $M_2$ and now I have an approximation of the posterior distribution for both model.
...
- 123
1
vote
1
answer
111
views
Weighting prior proposals based on distance function in approximate Bayesian computation
The typical approach in approximate Bayesian computation (ABC) is to propose parameters from the prior, simulate data $\chi'_\text{sim}$ and then accept data that minimises the data misfit $\lambda$ ...
- 1,235
0
votes
2
answers
2k
views
How do I calculate the weights in ABC-SMC
I have been reading through the Tutorial on ABC rejection and ABC SMC for parameter estimation and model selection by Tina Toni and Michael P. H. Stumpf.
I can't work out how to calculate the weights ...
- 129