# Regression model sampling solely one sample every 5th second

**URL:** <https://discourse.pymc.io/t/regression-model-sampling-solely-one-sample-every-5th-second/10234>\
**Category:** version agnostic\
**Tags:** modeling\
**Created:** [August 25, 2022, 2:21pm UTC](https://discourse.pymc.io/t/regression-model-sampling-solely-one-sample-every-5th-second/10234 "2022-08-25T14:21:26Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![steward](https://avatars.discourse-cdn.com/v4/letter/s/82dd89/32.png) [@steward](https://discourse.pymc.io/u/steward)\
**Post date:** [August 25, 2022, 2:21pm UTC](https://discourse.pymc.io/t/regression-model-sampling-solely-one-sample-every-5th-second/10234/1 "2022-08-25T14:21:26Z")

</div>

After trying to get pymc3 to work on windows 10 for several days without luck i decided to try v4. The installation seemed to work and i was no longer facing the “using numpy c-api based implementation for blas functions” . warning.

The main issue faced when using pymc3 was the speed, 1 sample was drawn every 4/5 seconds for my model using the NUTS-sampler.

The setup of the model before sampling usually took like 10 minutes or more.

The same issue appears to still be apperant for me in v4 making me think that the model itself is the problem. This is even more apparent if you let it run and get posteriors which are really far from the truth(the simulated values).

Maybe someone can point out whats the problem, it is probably the model but could also be my computer not matching up but most likely its the model due to my inexperience

\*\*UPDATE: . I tried to run the code in [GLM: Negative Binomial Regression — PyMC example gallery](https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-negative-binomial-regression.html) which took 177 seconds on my computer compared to their 47, quite a large difference still.  
First off i post the code and below you find the installations so you can reproduce:

```auto
import numpy as np
import math
import pandas as pd
from dateutil.tz import gettz
from datetime import datetime
import pymc as pm
from typing import List, Dict, Union
import aesara as theano
import aesara.tensor as tt
import arviz as az
import matplotlib.pyplot as plt

def make_seasonality(
    series_len, seasonality, method="discrete", order=3, duration=1, scale=0.05, seed=1
):
    if seasonality > 1:
        if method == "fourier":
            t = np.arange(0, series_len)
            out = []
            for i in range(1, order + 1):
                x = 2.0 * i * np.pi * t / seasonality
                out.append(np.sin(x))
                out.append(np.cos(x))
            out = np.column_stack(out)
            b = np.random.default_rng(seed).normal(0, scale, order * 2)
            seas = np.matmul(out, b)
        else:
            # initialization
            seas = []
            iterations = math.ceil(series_len / duration)
            # initialize vector to be repeated
            init_seas = np.zeros(seasonality)
            init_seas[:-1] = np.random.default_rng(seed).normal(
                0, scale, seasonality - 1
            )
            init_seas[seasonality - 1] = -1 * np.sum(init_seas)
            for idx in range(iterations):
                seas += [init_seas[idx % seasonality]] * duration
            seas = np.array(seas[:series_len])
    else:
        seas = np.zeros(series_len)
    return seas

def generatorcustom(n, verbose=True):
    def adstock(x: List, theta: float):
        x_decayed = [0 for _ in x]
        x_decayed[0] = x[0]

        for xi in range(1, len(x_decayed)):
            x_decayed[xi] = x[xi] + theta * x_decayed[xi - 1]

        return x_decayed

    def saturation(x, theta):
        return np.array(x) ** theta

    seed = 42
    np.random.seed(seed)
    # make the time varing coefs
    tau = np.arange(1, n + 1) / n
    data = pd.DataFrame({
        'tau': tau,
        'date': pd.date_range(start='1/1/2018', periods=n),
        'saturationx1': 0.34,
        'saturationx2': 0.65,
        'adstockx1': 0.8,
        'adstockx2': 0.32,
        'x1': 0 + np.abs(np.random.normal(400, 200, size=n)), #np.random.normal(0, 10, size=n),
        'x2': 0 + np.abs(np.random.normal(400, 200, size=n)), #np.random.normal(0, 10, size=n),
        'trend': np.cumsum(np.concatenate((np.array([1]), np.random.normal(0, 0.1, n - 1)))),
        'seasonality7': make_seasonality(series_len=n, seasonality=7, duration=1, method='discrete', seed=2020),
        'seasonality30': make_seasonality(series_len=n, seasonality=30, duration=1, method='discrete', seed=2020),
    })

    x1_ = data['x1']
    adstockx1_ = data['adstockx1']
    saturationx1_ = data['saturationx1']
    x2_ = data['x2']
    adstockx2_ = data['adstockx2']
    saturationx2_ = data['saturationx2']

    x1adstocked = adstock(x1_, adstockx1_[0])
    x1 = saturation(x1adstocked, saturationx1_)
    x2adstocked = adstock(x2_, adstockx2_[0])
    x2 = saturation(x2adstocked, saturationx2_)

    response = math.e **(data['trend']) * math.e** (data['seasonality7'] + data['seasonality30']) * x1 * x2

    df = pd.DataFrame.from_dict(data={'x1': x1, 'x2': x2, 'response': response, 'date': data['date']})
    df.index = df['date']

    df.plot()
    plt.show()
    df['response'].plot()
    plt.show()
    df['x1'].plot()
    plt.show()
    df['x2'].plot()
    plt.show()

    return df

def removeleadingandtrailingnans(frame: pd.DataFrame, indicator: Union[List, str]="facost") -> pd.DataFrame:
    # remove leading and trailing NaNs
    return frame.loc[frame[indicator].first_valid_index():frame[indicator].last_valid_index()]

class TorchStandardScaler:
    def __init__ (self, with_std=False):
        self.with_std = with_std

    def fit(self, x):
        self.mean = x.mean()
        self.std = x.std()

    def transform(self, x):
        x -= self.mean
        if not self.with_std:
            return x
        x /= (self.std + 1e-7)
        return x

    def fittransform(self, x):
        self.fit(x=x)
        return self.transform(x=x)

    def inverse_transform(self, x):
        x += self.mean
        if not self.with_std:
            return x
        x *= (self.std + 1e-7)
        return x

def getprocessedframe(frame, nowianatimezone, horizon, applylog=True, applynormalizing=True, applylogtotalconv=True):
    if 'totalconvresponse' not in frame:
        frame['totalconvresponse'] = frame['goconvresponse'] + frame['faconvresponse']

    frame = removeleadingandtrailingnans(frame=frame,
                                         indicator='facost')
    frame = removeleadingandtrailingnans(frame=frame,
                                         indicator='gocost')

    frame = frame[frame['totalconvresponse'].notna()] # changes the coefficients completely w.r.t go and fb

    s = pd.DatetimeIndex(frame.index)
    frame.index = s
    daterange = pd.date_range(start=s.tolist()[0],
                              end=s.tolist()[-1])
    frame = frame.reindex(daterange)

    frame['date'] = frame.index

    frame = frame.fillna(0.0)
    frame['day'] = pd.to_datetime(frame.index).day.tolist()
    frame['month'] = pd.to_datetime(frame.index).month.tolist()
    data = frame
    data['facost'] += 1
    data['gocost'] += 1
    data['day'] += 1
    data['month'] += 1
    data['totalconvresponse'] += 1
    data['roas'] = data['totalconvresponse'] / (data['facost'] + data['gocost'])
    if applylogtotalconv:
        frame[['totalconvresponse']] = frame[['totalconvresponse']].apply(np.log)
    if applylog:
        regressors = ['facost', 'gocost']
        frame[["totalconvresponse"] + regressors] = frame[["totalconvresponse"] + regressors].apply(np.log)
    if applynormalizing:
        frame['unprocessedfacost'] = frame['facost'].values
        fascaler = TorchStandardScaler(with_std=False)
        frame['facost'] = fascaler.fittransform(frame['facost'].values.reshape(-1, 1))

        frame['unprocessedgocost'] = frame['gocost'].values
        goscaler = TorchStandardScaler(with_std=False)
        frame['gocost'] = goscaler.fittransform(frame['gocost'].values.reshape(-1, 1))
    else:
        fascaler = None
        goscaler = None
    return data, (fascaler, goscaler)

def adstock_geometric_theano_pymc3(x, theta):
    x = tt.as_tensor_variable(x)

    def adstock_geometric_recurrence_theano(index, input_x, decay_x, theta):
        return tt.set_subtensor(decay_x[index], tt.sum(input_x + theta * decay_x[index - 1]))

    len_observed = x.shape[0]

    x_decayed = tt.zeros_like(x)
    x_decayed = tt.set_subtensor(x_decayed[0], x[0])

    output, _ = theano.scan(
        fn=adstock_geometric_recurrence_theano,
        sequences=[tt.arange(1, len_observed), x[1:len_observed]],
        outputs_info=x_decayed,
        non_sequences=theta,
        n_steps=len_observed - 1
    )

    return output[-1]

def runknot(frame, nowianatimezone,
            horizon=30, target='totalconvresponse'):

    data, (_, _) = getprocessedframe(frame=frame,
                                     nowianatimezone=nowianatimezone,
                                     horizon=horizon,
                                     applynormalizing=True,
                                     applylog=True,
                                     applylogtotalconv=False)

    data['dayofweek'] = data.index.day_of_week.tolist()
    data['dayinmonth'] = data.index.day.tolist()

    n_order = 3
    periods_weekday = data["dayofweek"] / 7
    fourier_features_weekday = pd.DataFrame(
        {
            f"{func}_order_{order}": getattr(np, func)(2 * np.pi * periods_weekday * order)
            for order in range(1, n_order + 1)
            for func in ("sin", "cos")
        }
    )

    n_order = 3
    periods_dayinmonth = data['dayinmonth'] / 30
    fourier_features_dayinmonth = pd.DataFrame(
        {
            f"{func}_order_{order}": getattr(np, func)(2 * np.pi * periods_dayinmonth * order)
            for order in range(1, n_order + 1)
            for func in ("sin", "cos")
        }
    )

    t = np.array((data.index - data.index.min()) / (data.index.max() - data.index.min()))

    START_ANALYSIS_INDEX = data.index.tolist()[0]
    END_ANALYSIS_INDEX = data.index.tolist()[-1]

    knots = ['25-2', '2-9', '9-16', '16-25'] # 4 weeks split

    response_mean = []

    date = data['date'].to_numpy()
    coords = {'date': date, 'fourier_mode': np.arange(2 * n_order)}

    with pm.Model(coords=coords) as model_2:
        facost = data['unprocessedfacost'].values
        gocost = data['unprocessedgocost'].values

        adstockfa = pm.Beta('faadstock', 3, 3)
        adstockgo = pm.Beta('goadstock', 3, 3)

        facostadstocked = adstock_geometric_theano_pymc3(x=facost, theta=adstockfa)
        falogged = np.log1p(facostadstocked)
        fafullyprocessed = falogged / facost.mean()

        gocostadstocked = adstock_geometric_theano_pymc3(x=gocost, theta=adstockgo)
        gologged = np.log1p(gocostadstocked)
        gofullyprocessed = gologged / gocost.mean()

        saturationsgo = [1 for _ in gocost]
        saturationsfa = [1 for _ in facost]

        # seasonality weekday
        b_fourier_weekday = pm.Laplace(name="b_fourier_weekday", mu=0, b=2,
                                       dims="fourier_mode")

        # seasonality dayinmonth
        b_fourier_dayinmonth = pm.Laplace(name="b_fourier_dayinmonth", mu=0, b=2,
                                          dims="fourier_mode")

        # trend
        b_trend = pm.Normal(name="b_trend", mu=0, sigma=2)

        for knot in knots:
            firstidx, lastidx = (int(x) for x in knot.split('-'))
            if firstidx > lastidx:
                mask = (data.index.day > firstidx) | (data.index.day <= lastidx)
            else:
                mask = (data.index.day > firstidx) & (data.index.day <= lastidx)

            saturationgo = pm.Beta(f'gosaturation-knot-{knot}', 3, 3)
            saturationfa = pm.Beta(f'fasaturation-knot-{knot}', 3, 3)
            indexes = np.nonzero(mask)[0]
            for idx in indexes:
                saturationsgo[idx] = saturationgo
                saturationsfa[idx] = saturationfa

        fa = fafullyprocessed.dot(saturationsfa)
        go = gofullyprocessed.dot(saturationsgo)

        response_mean.append(fa)
        response_mean.append(go)

        trend = pm.Deterministic(name="trend", var=b_trend * t, dims="date")
        seasonalityweekday = pm.Deterministic(name="seasonality_dayinweek",
                                              var=pm.math.dot(fourier_features_weekday, b_fourier_weekday),
                                              dims="date")
        seasonalitydayinmonth = pm.Deterministic(name="seasonality_dayinmonth",
                                                 var=pm.math.dot(fourier_features_dayinmonth, b_fourier_dayinmonth),
                                                 dims="date")
        mu = pm.Deterministic(name="mu",
                              var=np.sum(response_mean) + trend + seasonalityweekday + seasonalitydayinmonth,
                              dims="date")

        sigma = pm.HalfNormal(name="sigma", sigma=10)

        nu = pm.Gamma(name="nu", alpha=25, beta=2)

        likelihood = pm.StudentT(name="outcome",
                                 nu=nu,
                                 mu=mu,
                                 sigma=sigma,
                                 observed=data['totalconvresponse'],
                                 dims="date")

    with model_2:
        idata = pm.sample(1000,
                          cores=1,
                          # tune=1000,
                          # step=None,
                          # target_accept=0.95,
                          return_inferencedata=True)

        idata.extend(pm.sample_prior_predictive())
        idata.extend(pm.sample_posterior_predictive(idata))

    import arviz as az
    az.plot_trace(idata)
    az.plot_forest(idata)
    az.plot_posterior(idata)

def run():
    df = generatorcustom(n=500)
    df['facost'] = df['x1']
    df['gocost'] = df['x2']
    df['totalconvresponse'] = df['response']

    now = datetime.now(tz=gettz(name='Europe/Helsinki')).date()

    runknot(frame=df,
            nowianatimezone=now,
            horizon=30)

if __name__ == ' __main__':
    run()

```

installations:

# Name Version Build Channel

aeppl 0.0.35 pypi\_0 pypi  
aesara 2.8.2 py39h7171602\_0 conda-forge  
aesara-base 2.8.2 py39hcbf5309\_0 conda-forge  
arviz 0.12.1 pypi\_0 pypi  
blas 2.116 mkl conda-forge  
blas-devel 3.9.0 16\_win64\_mkl conda-forge  
bzip2 1.0.8 h8ffe710\_4 conda-forge  
ca-certificates 2022.6.15 h5b45459\_0 conda-forge  
cachetools 5.2.0 pypi\_0 pypi  
cairo 1.16.0 h0ac17fb\_1012 conda-forge  
cftime 1.6.1 pypi\_0 pypi  
cloudpickle 2.1.0 pypi\_0 pypi  
cons 0.4.5 pyhd8ed1ab\_0 conda-forge  
cycler 0.11.0 pypi\_0 pypi  
deprecat 2.1.1 pypi\_0 pypi  
dill 0.3.5.1 pypi\_0 pypi  
etuples 0.3.5 pyhd8ed1ab\_0 conda-forge  
expat 2.4.8 h39d44d4\_0 conda-forge  
fastprogress 1.0.3 pypi\_0 pypi  
filelock 3.8.0 pyhd8ed1ab\_0 conda-forge  
font-ttf-dejavu-sans-mono 2.37 hab24e00\_0 conda-forge  
font-ttf-inconsolata 3.000 h77eed37\_0 conda-forge  
font-ttf-source-code-pro 2.038 h77eed37\_0 conda-forge  
font-ttf-ubuntu 0.83 hab24e00\_0 conda-forge  
fontconfig 2.14.0 hce3cb01\_0 conda-forge  
fonts-conda-ecosystem 1 0 conda-forge  
fonts-conda-forge 1 0 conda-forge  
fonttools 4.37.1 pypi\_0 pypi  
freetype 2.12.1 h546665d\_0 conda-forge  
fribidi 1.0.10 h8d14728\_0 conda-forge  
getopt-win32 0.1 h8ffe710\_0 conda-forge  
gettext 0.19.8.1 ha2e2712\_1008 conda-forge  
graphite2 1.3.13 1000 conda-forge  
graphviz 5.0.1 had6c3a3\_0 conda-forge  
gts 0.7.6 h7c369d9\_2 conda-forge  
harfbuzz 5.1.0 h27de254\_0 conda-forge  
icu 70.1 h0e60522\_0 conda-forge  
intel-openmp 2022.1.0 h57928b3\_3787 conda-forge  
jpeg 9e h8ffe710\_2 conda-forge  
kiwisolver 1.4.4 pypi\_0 pypi  
lerc 4.0.0 h63175ca\_0 conda-forge  
libblas 3.9.0 16\_win64\_mkl conda-forge  
libcblas 3.9.0 16\_win64\_mkl conda-forge  
libdeflate 1.13 h8ffe710\_0 conda-forge  
libffi 3.4.2 h8ffe710\_5 conda-forge  
libgd 2.3.3 h891f43f\_3 conda-forge  
libglib 2.72.1 h3be07f2\_0 conda-forge  
libiconv 1.16 he774522\_0 conda-forge  
liblapack 3.9.0 16\_win64\_mkl conda-forge  
liblapacke 3.9.0 16\_win64\_mkl conda-forge  
libpng 1.6.37 h1d00b33\_4 conda-forge  
libpython 2.2 py39hcbf5309\_1 conda-forge  
libsqlite 3.39.2 h8ffe710\_1 conda-forge  
libtiff 4.4.0 h92677e6\_3 conda-forge  
libwebp 1.2.4 h8ffe710\_0 conda-forge  
libwebp-base 1.2.4 h8ffe710\_0 conda-forge  
libxcb 1.13 hcd874cb\_1004 conda-forge  
libzlib 1.2.12 h8ffe710\_2 conda-forge  
llvmlite 0.38.1 py39ha0cd8c8\_0 conda-forge  
logical-unification 0.4.5 pyhd8ed1ab\_0 conda-forge  
m2w64-binutils 2.25.1 5 conda-forge  
m2w64-bzip2 1.0.6 6 conda-forge  
m2w64-crt-git 5.0.0.4636.2595836 2 conda-forge  
m2w64-gcc 5.3.0 6 conda-forge  
m2w64-gcc-ada 5.3.0 6 conda-forge  
m2w64-gcc-fortran 5.3.0 6 conda-forge  
m2w64-gcc-libgfortran 5.3.0 6 conda-forge  
m2w64-gcc-libs 5.3.0 7 conda-forge  
m2w64-gcc-libs-core 5.3.0 7 conda-forge  
m2w64-gcc-objc 5.3.0 6 conda-forge  
m2w64-gmp 6.1.0 2 conda-forge  
m2w64-headers-git 5.0.0.4636.c0ad18a 2 conda-forge  
m2w64-isl 0.16.1 2 conda-forge  
m2w64-libiconv 1.14 6 conda-forge  
m2w64-libmangle-git 5.0.0.4509.2e5a9a2 2 conda-forge  
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 conda-forge  
m2w64-make 4.1.2351.a80a8b8 2 conda-forge  
m2w64-mpc 1.0.3 3 conda-forge  
m2w64-mpfr 3.1.4 4 conda-forge  
m2w64-pkg-config 0.29.1 2 conda-forge  
m2w64-toolchain 5.3.0 7 conda-forge  
m2w64-toolchain\_win-64 2.4.0 0 conda-forge  
m2w64-tools-git 5.0.0.4592.90b8472 2 conda-forge  
m2w64-windows-default-manifest 6.4 3 conda-forge  
m2w64-winpthreads-git 5.0.0.4634.697f757 2 conda-forge  
m2w64-zlib 1.2.8 10 conda-forge  
matplotlib 3.5.3 pypi\_0 pypi  
minikanren 1.0.3 pyhd8ed1ab\_0 conda-forge  
mkl 2022.1.0 h6a75c08\_874 conda-forge  
mkl-devel 2022.1.0 h57928b3\_875 conda-forge  
mkl-include 2022.1.0 h6a75c08\_874 conda-forge  
mkl-service 2.4.0 py39h0fb9bb3\_0 conda-forge  
msys2-conda-epoch 20160418 1 conda-forge  
multipledispatch 0.6.0 py\_0 conda-forge  
netcdf4 1.6.0 pypi\_0 pypi  
numba 0.55.2 py39hb8cd55e\_0 conda-forge  
numpy 1.22.1 pypi\_0 pypi  
openssl 3.0.5 h8ffe710\_1 conda-forge  
packaging 21.3 pypi\_0 pypi  
pandas 1.4.3 pypi\_0 pypi  
pango 1.50.9 h480d202\_0 conda-forge  
patsy 0.5.2 pypi\_0 pypi  
pcre 8.45 h0e60522\_0 conda-forge  
pillow 9.2.0 pypi\_0 pypi  
pip 22.2.2 pyhd8ed1ab\_0 conda-forge  
pixman 0.40.0 h8ffe710\_0 conda-forge  
pthread-stubs 0.4 hcd874cb\_1001 conda-forge  
pymc 4.1.6 pypi\_0 pypi  
pymc3 3.11.5 pypi\_0 pypi  
pyparsing 3.0.9 pypi\_0 pypi  
python 3.9.13 hcf16a7b\_0\_cpython conda-forge  
python-dateutil 2.8.2 pypi\_0 pypi  
python-graphviz 0.20.1 pyh22cad53\_0 conda-forge  
python\_abi 3.9 2\_cp39 conda-forge  
pytz 2022.2.1 pypi\_0 pypi  
scipy 1.7.3 pypi\_0 pypi  
semver 2.13.0 pypi\_0 pypi  
setuptools 65.3.0 py39hcbf5309\_0 conda-forge  
six 1.16.0 pyh6c4a22f\_0 conda-forge  
sqlite 3.39.2 h8ffe710\_1 conda-forge  
statsmodels 0.13.2 pypi\_0 pypi  
tbb 2021.5.0 h2d74725\_1 conda-forge  
theano-pymc 1.1.2 pypi\_0 pypi  
tk 8.6.12 h8ffe710\_0 conda-forge  
toolz 0.12.0 pyhd8ed1ab\_0 conda-forge  
typing\_extensions 4.3.0 pyha770c72\_0 conda-forge  
tzdata 2022c h191b570\_0 conda-forge  
ucrt 10.0.20348.0 h57928b3\_0 conda-forge  
vc 14.2 hb210afc\_6 conda-forge  
vs2015\_runtime 14.29.30037 h902a5da\_6 conda-forge  
wheel 0.37.1 pyhd8ed1ab\_0 conda-forge  
wrapt 1.14.1 pypi\_0 pypi  
xarray 2022.6.0 pypi\_0 pypi  
xarray-einstats 0.3.0 pypi\_0 pypi  
xorg-kbproto 1.0.7 hcd874cb\_1002 conda-forge  
xorg-libice 1.0.10 hcd874cb\_0 conda-forge  
xorg-libsm 1.2.3 hcd874cb\_1000 conda-forge  
xorg-libx11 1.7.2 hcd874cb\_0 conda-forge  
xorg-libxau 1.0.9 hcd874cb\_0 conda-forge  
xorg-libxdmcp 1.1.3 hcd874cb\_0 conda-forge  
xorg-libxext 1.3.4 hcd874cb\_1 conda-forge  
xorg-libxpm 3.5.13 hcd874cb\_0 conda-forge  
xorg-libxt 1.2.1 hcd874cb\_2 conda-forge  
xorg-xextproto 7.3.0 hcd874cb\_1002 conda-forge  
xorg-xproto 7.0.31 hcd874cb\_1007 conda-forge  
xz 5.2.6 h8d14728\_0 conda-forge  
zlib 1.2.12 h8ffe710\_2 conda-forge  
zstd 1.5.2 h7755175\_4 conda-forge

---

<div class="post-metadata">

**Author:** ![steward](https://avatars.discourse-cdn.com/v4/letter/s/82dd89/32.png) [@steward](https://discourse.pymc.io/u/steward)\
**Post date:** [August 27, 2022, 9:59am UTC](https://discourse.pymc.io/t/regression-model-sampling-solely-one-sample-every-5th-second/10234/2 "2022-08-27T09:59:35Z")

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one obvious error in the code is  
df = pd.DataFrame.from\_dict(data={‘x1’: x1, ‘x2’: x2, ‘response’: response, ‘date’: data[‘date’]})  
x1 and x2 is already adstocked and saturated here…  
should be:  
df = pd.DataFrame.from\_dict(data={‘x1’: data[‘x1’], ‘x2’: data[‘x2’], ‘response’: response, ‘date’: data[‘date’]})

will update how it effects the runtime… considering the mismatch of the data-generation-process and the model it may improve it a little bit.

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**Author:** ![steward](https://avatars.discourse-cdn.com/v4/letter/s/82dd89/32.png) [@steward](https://discourse.pymc.io/u/steward)\
**Post date:** [August 29, 2022, 12:14am UTC](https://discourse.pymc.io/t/regression-model-sampling-solely-one-sample-every-5th-second/10234/3 "2022-08-29T00:14:00Z")

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in general the model does not match the data-generation-process(the simulation) which is the reason behind the poor posterior estimates, reviewing the generated trend and the modelling of it in the model we can easily see a mismatch where as the simulated trend is much more complex than what the modelling of it in the model is able to capture, it effects the runtime aswell but not as drastic as one would believe.