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Also fix CSV precision bug in matrix outputs
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name,Mean,MCSE,StdDev,MAD,5%,50%,95%,ESS_bulk,ESS_tail,R_hat | ||
"lp__",-15.3787,1.17576,3.71807,0.779848,-20.9245,-14.8305,-12.6946,5,5,1.31109 | ||
"accept_stat__",0.68789,0.0961912,0.304183,0.252626,0.208269,0.779947,0.989055,5,5,1.05408 | ||
"stepsize__",0.894277,nan,1.17028e-16,0,0.894277,0.894277,0.894277,nan,nan,nan | ||
"treedepth__",0.9,0.1,0.316228,0,0.45,1,1,nan,nan,nan | ||
"x[1]",0.608916,0.0690549,0.218371,0.108465,0.284524,0.666485,0.866975,5,5,0.905216 | ||
"x[2]",0.479369,0.051013,0.161317,0.164219,0.263389,0.451793,0.686842,5,5,1.00156 | ||
"y[1,1]",0.425556,0.124575,0.393941,0.150819,0.0588995,0.164637,0.926039,5,5,1.431 | ||
"y[1,2]",0.595682,0.0758338,0.239808,0.178212,0.249647,0.663856,0.850511,5,5,1.11989 | ||
"y[1,3]",0.473713,0.104663,0.330973,0.39317,0.0677836,0.483405,0.863312,5,5,0.986085 | ||
"y[2,1]",0.511464,0.0868387,0.274608,0.383569,0.167417,0.487338,0.895304,5,5,1.43105 | ||
"y[2,2]",0.559192,0.0872593,0.275938,0.307409,0.230475,0.525767,0.932888,5,5,0.915761 | ||
"y[2,3]",0.51956,0.0997935,0.315575,0.212694,0.0361943,0.536618,0.930452,5,5,1.32719 | ||
# Inference for Stan model: issue_342_model | ||
# 1 chains: each with iter=10; warmup=1000; thin=1; 10 iterations saved. | ||
# | ||
# Warmup took 0.23 seconds | ||
# Sampling took 0.0018 seconds | ||
# Samples were drawn using hmc with nuts. | ||
# For each parameter, ESS_bulk and ESS_tail measure the effective sample size | ||
for the entire sample (bulk) and for the the .05 and .95 tails (tail), | ||
# and R_hat measures the potential scale reduction on split chains. | ||
At convergence R_hat will be very close to 1.00. |
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Inference for Stan model: issue_342_model | ||
1 chains: each with iter=10; warmup=1000; thin=1; 10 iterations saved. | ||
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Warmup took 0.23 seconds | ||
Sampling took 0.0018 seconds | ||
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||
Mean MCSE StdDev MAD 5% 50% 95% ESS_bulk ESS_tail R_hat | ||
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lp__ -15 1.2 3.7 0.78 -21 -15 -13 5.0 5.0 1.3 | ||
accept_stat__ 0.69 0.096 3.0e-01 0.25 0.21 0.78 0.99 5.0 5.0 1.1 | ||
stepsize__ 0.89 nan 1.2e-16 0.00 0.89 0.89 0.89 nan nan nan | ||
treedepth__ 0.90 0.10 3.2e-01 0.00 0.45 1.0 1.0 nan nan nan | ||
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x[1] 0.61 0.069 0.22 0.11 0.28 0.67 0.87 5.0 5.0 0.91 | ||
x[2] 0.48 0.051 0.16 0.16 0.26 0.45 0.69 5.0 5.0 1.0 | ||
y[1,1] 0.43 0.12 0.39 0.15 0.059 0.16 0.93 5.0 5.0 1.4 | ||
y[1,2] 0.60 0.076 0.24 0.18 0.25 0.66 0.85 5.0 5.0 1.1 | ||
y[1,3] 0.47 0.10 0.33 0.39 0.068 0.48 0.86 5.0 5.0 0.99 | ||
y[2,1] 0.51 0.087 0.27 0.38 0.17 0.49 0.90 5.0 5.0 1.4 | ||
y[2,2] 0.56 0.087 0.28 0.31 0.23 0.53 0.93 5.0 5.0 0.92 | ||
y[2,3] 0.52 0.100 0.32 0.21 0.036 0.54 0.93 5.0 5.0 1.3 | ||
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||
Samples were drawn using hmc with nuts. | ||
For each parameter, ESS_bulk and ESS_tail measure the effective sample size | ||
for the entire sample (bulk) and for the the .05 and .95 tails (tail), | ||
and R_hat measures the potential scale reduction on split chains. | ||
At convergence R_hat will be very close to 1.00. |
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