In [1]:

    

include("ilmtools.jl")


    

    












    












    
Out[1]:





create_loglikelihood (generic function with 1 method)

In [2]:

    

pop_db1 = create_pop_db_univariates(100, Uniform(0,10), Uniform(0,10))


    

    
Out[2]:




ind_id
x
y
1
1
9.467709189778876
9.549770698293207
2
2
3.6747219101128925
7.209528937919522
3
3
0.8315474096917708
9.623270101299983
4
4
4.367377563511454
6.786115040344695
5
5
4.552125575673649
7.463773708972887
6
6
7.127112049951025
3.588989446624209
7
7
7.881581319863063
7.120939338384657
8
8
7.182790432761063
8.140282873336812
9
9
1.760489408467194
3.950723955388342
10
10
5.292474158923173
0.4738983583632095
11
11
5.171941382257925
3.244323557927702
12
12
3.0568309349866074
6.272177042587808
13
13
7.013321562798273
5.310445131681789
14
14
1.635135983268432
4.07545352679035
15
15
1.511297376756402
9.347927468767077
16
16
2.0521597979695283
4.064515559623684
17
17
8.99811909030484
4.80569851356851
18
18
8.967608250263464
0.22772152148523128
19
19
3.0343664392676795
9.082287994918595
20
20
1.061936280297131
5.540994220585944
21
21
5.828381760497939
1.4075768552927048
22
22
6.229484261310199
9.191133454871284
23
23
6.967696712464983
8.16582783290955
24
24
9.890146716518542
7.337257742284249
25
25
9.164989130505925
1.6326231404762925
26
26
5.46496146235238
2.2768032662019433
27
27
1.0579896262169064
6.474824765924869
28
28
4.376980884916648
2.8516448250674764
29
29
3.8812599324347263
4.739342412261225
30
30
5.440603055041131
3.855145371422426
&vellip
&vellip
&vellip
&vellip

In [3]:

    

evdb = infect_recover_loop(pop_db1, "continuous", "SI", 2, 6)


    

    
Out[3]:





edb(100x3 DataFrame
| Row | ind_id | s | i       |
|-----|--------|---|---------|
| 1   | 1      | 0 | 1.63338 |
| 2   | 2      | 0 | 1.55827 |
| 3   | 3      | 0 | 3.15572 |
| 4   | 4      | 0 | 1.50927 |
| 5   | 5      | 0 | 1.53509 |
| 6   | 6      | 0 | 1.27439 |
| 7   | 7      | 0 | 1.61051 |
| 8   | 8      | 0 | 1.55885 |
| 9   | 9      | 0 | 1.60665 |
| 10  | 10     | 0 | 1.26589 |
| 11  | 11     | 0 | 1.0245  |
⋮
| 89  | 89     | 0 | 1.6076  |
| 90  | 90     | 0 | 1.61267 |
| 91  | 91     | 0 | 1.6076  |
| 92  | 92     | 0 | 3.44986 |
| 93  | 93     | 0 | 1.0     |
| 94  | 94     | 0 | 1.56291 |
| 95  | 95     | 0 | 1.27827 |
| 96  | 96     | 0 | 1.61076 |
| 97  | 97     | 0 | 1.61248 |
| 98  | 98     | 0 | 1.70283 |
| 99  | 99     | 0 | 1.6371  |
| 100 | 100    | 0 | 1.26071 |,[1.0,1.00001,1.003,1.00492,1.00892,1.0245,1.06725,1.07877,1.08093,1.16184  …  2.09353,2.114,2.11895,2.18509,2.26525,2.75425,3.11534,3.15572,3.44986,5.27512],"continuous")

In [4]:

    

ll = create_loglikelihood(pop_db1, evdb)


    

    
Out[4]:





cSI_ll (generic function with 1 method)

In [5]:

    

ll((2,6))
@time ll((2,6))


    

    




elapsed time: 0.00219451 seconds (1815784 bytes allocated)






    
Out[5]:





337.01549435030125

In [6]:

    

n = 20000
burnin = 10000
sim = Chains(n, 2, names = ["alpha", "beta"])
theta = AMMVariate([2.0, 6.0])
SigmaF = cholfact(eye(2))
@time for i in 1:n
    amm!(theta, SigmaF, ll, adapt = (i <= burnin))
    sim[i,:,1] = [theta[1:2]]
end
Mamba.describe(sim)


    

    




elapsed time: 135.180494512 seconds (72151537116 bytes allocated, 53.34% gc time)
Iterations = 1:20000
Thinning interval = 1
Chains = 1
Samples per chain = 20000

Empirical Posterior Estimates:
3x6 Array{Any,2}:
 ""        "Mean"   "SD"      "Naive SE"   "MCSE"        "ESS"
 "alpha"  2.24519  0.270152  0.00191026   0.0052094  7333.9   
 "beta"   5.87499  0.140049  0.000990296  0.0026809  7387.8   

Quantiles:
3x6 Array{Any,2}:
 ""        "2.5%"   "25.0%"   "50.0%"   "75.0%"   "97.5%"
 "alpha"  1.75872  2.05405   2.23255   2.42002   2.79984 
 "beta"   5.59461  5.78013   5.87742   5.97154   6.14232 

In [7]:

    

plot(x=burnin:n, y=sim.value[burnin:n, 1, 1], Geom.line)


    

    
Out[7]:

In [8]:

    

plot(x=burnin:n, y=sim.value[burnin:n, 2, 1], Geom.line)


    

    
Out[8]: