ABC stands for Approximate Bayesian Computation. It is a computational technique for approximately simulating from a posterior distribution.
6
votes
1answer
474 views
ABC model selection
It has been shown that ABC model choice using Bayes factors is not to be recommended due to the presence of an error coming from the use of summary statistics. The conclusion in this paper relies on ...
3
votes
2answers
256 views
How to choose the tolerance parameter for ABC?
I have the following sorted data (sampling from parametric space [1,5]) with respect to their distances of parameter Theta. i.e.,
Let say N = 1000,
Theta : 1.1, 1.7, 1.9, 2.4, 2.8, . . . , 4.9
...
2
votes
1answer
187 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 ...
1
vote
0answers
44 views
Building artificial state space model from noise-less data
I have a discrete time stochastic process, where at each time the state of the system $X_t$
is given by:
$$
X_t = f_\theta(X_{t-1},\epsilon_t), \; \; \text{for} \; t = 1,\dots,T
$$
and, for example, ...
0
votes
0answers
34 views
Flexible multivariate parametric density
Suppose I have observed a vector-valued data point $y_{obs}$ from a statistical model:
$$
y \sim f(\theta)
$$
where $\theta$ are the unknown model parameters.
I would like to estimate $\theta$, but ...
