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Dominik Wabersich
Dominik Wabersich
  • 36
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The jags sampling algorithm is propably getting stuck in a local maximum.

2 possible solutions come to my mind:

  • Try using a different sampling algorithm that is better in handling this kind of situation.
  • Try using more chains, a bigger burnin period and larger thinning values (with parallel computing on a cluster, otherwise not practicable): this could go as far as using only the last sample per chain and running many many chains.

2 possible solutions come to my mind:

  • Try using a different sampling algorithm that is better in handling this kind of situation.
  • Try using more chains, a bigger burnin period and larger thinning values (with parallel computing on a cluster, otherwise not practicable): this could go as far as using only the last sample per chain and running many many chains.

The jags sampling algorithm is propably getting stuck in a local maximum.

2 possible solutions come to my mind:

  • Try using a different sampling algorithm that is better in handling this kind of situation.
  • Try using more chains, a bigger burnin period and larger thinning values (with parallel computing on a cluster, otherwise not practicable): this could go as far as using only the last sample per chain and running many many chains.
Source Link
Dominik Wabersich
Dominik Wabersich
  • 36
  • 4

2 possible solutions come to my mind:

  • Try using a different sampling algorithm that is better in handling this kind of situation.
  • Try using more chains, a bigger burnin period and larger thinning values (with parallel computing on a cluster, otherwise not practicable): this could go as far as using only the last sample per chain and running many many chains.