diff --git a/README.md b/README.md
index 3b71d37..de58717 100644
--- a/README.md
+++ b/README.md
@@ -120,7 +120,7 @@ Computes given integral with the limits of integration being 0 and 1 using Rando
### Usage:
```Python
-from src.algorithms.support_algorithms.rqmc import RQMC
+from src.procedures.support.rqmc import RQMC
rqmc = RQMC(lambda x: x**3 - x**2 + 1, error_tolerance=1e-5)
```
So `rqmc()[0]` is estimated integral value and `rqmc()[1]` is current error tolerance.
diff --git a/jupyter_examples/MLE Analysis of Bitcoin Data via PySATL-NMVM.ipynb b/jupyter_examples/MLE Analysis of Bitcoin Data via PySATL-NMVM.ipynb
new file mode 100644
index 0000000..65e765b
--- /dev/null
+++ b/jupyter_examples/MLE Analysis of Bitcoin Data via PySATL-NMVM.ipynb
@@ -0,0 +1,762 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7d7aebd7-ddb0-4ebb-8ad5-5645222f55cb",
+ "metadata": {},
+ "source": [
+ "# MLE Analysis of Bitcoin Data via PySATL-NMVM"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "4fb2a7c1-1a0d-4d71-953e-db00b6b2d6d9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "import numpy as np\n",
+ "import mplfinance as mpf\n",
+ "from matplotlib import pyplot as plt\n",
+ "from scipy.stats import fit\n",
+ "from scipy.stats import genhyperbolic\n",
+ "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
+ "from src.estimators.semiparametric.nmv_semiparametric_estimator import NMVSemiParametricEstimator\n",
+ "from src.estimators.semiparametric.nm_semiparametric_estimator import NMSemiParametricEstimator\n",
+ "from src.algorithms.semiparam_algorithms.nm_semi_param_algorithms.sigma_estimation_eigenvalue_based import SemiParametricMeanSigmaEstimationEigenvalueBased"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fed5d043-1162-4db4-9739-df74741e7542",
+ "metadata": {},
+ "source": [
+ "Изначальные данные $-$ это сделки на бирже ByBit по торговой паре BTC/USDT за период 24.04.2025-24.05.2025. Всего наблюдений порядка $23\\cdot10^6$\n",
+ "\n",
+ "Данные были агрегированные в бары по 10000\\$, из каждого выписаны основные показатели:\n",
+ "- $open$: цена открытия первой сделки в баре\n",
+ "- $close$: цена закрытия последней сделки в баре\n",
+ "- $low$: минимальная стоимость биткоина среди всех сделок в баре\n",
+ "- $high$: максимальная стоимость биткоина среди всех сделок в баре\n",
+ "- $volume$: суммарный объем сделок в баре\n",
+ "- $legth$: количество сделок в баре\n",
+ "\n",
+ "На основе нового датасета добавлен ещё один столб:\n",
+ "- $h_n$: возврат - разница между ценами закрытия $n$-ой и $(n-1)$-ой сделками\n",
+ "\n",
+ "В новом датасете получилось около $33\\cdot10^3$ наблюдений\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "12ec03ab-d97d-4a12-9392-e8fde28b0a59",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(\"data/bitcoin_example.csv\", index_col=\"bar\")\n",
+ "df['hn'] = df['close'] - df['close'].shift(1)\n",
+ "df.loc[0, 'hn'] = 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "86084818-17bc-4385-92da-5636e599b962",
+ "metadata": {},
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