diff --git a/examples/08_example_optimize_prior .ipynb b/examples/08_example_optimize_prior .ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../')\n",
+    "import sportran as st\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import CubicSpline, interp1d\n",
+    "def model_scalar(x, y):\n",
+    "    return CubicSpline(np.concatenate([-x[::-1], x[1:]]), np.concatenate([y[::-1], y[1:]]))\n",
+    "\n",
+    "n = 2\n",
+    "def model_wishart_(x, y):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    y00, y01, y11 = y.reshape(3,l)\n",
+    "    yy = np.array([[y00, y01], [np.zeros_like(y01), y11]]).T\n",
+    "    # yy = np.einsum('tab,tbc->tac', np.transpose(yy, axes=(0,2,1)), yy)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    # return interp1d(xx, yy)) \n",
+    "    return CubicSpline(xx, yy) #, bc_type = 'clamped')\n",
+    "\n",
+    "import numpy as np\n",
+    "from scipy.interpolate import CubicSpline\n",
+    "\n",
+    "def model_wishart(x, y, N):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    upper_triangle_indices = np.triu_indices(N)\n",
+    "    y_elements = y.reshape(len(upper_triangle_indices[0]), l)\n",
+    "    \n",
+    "    yy = np.zeros((l, N, N))\n",
+    "    for k, (i, j) in enumerate(zip(*upper_triangle_indices)):\n",
+    "        yy[:, j, i] = y_elements[k]\n",
+    "    \n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    return CubicSpline(xx, yy)\n",
+    "\n",
+    "def mini_model_w(x, y, N):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    \n",
+    "    # y.shape should be l*N**2\n",
+    "    yy = y.reshape(l, N**2)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    return CubicSpline(xx, yy)\n",
+    "\n",
+    "def mini_model_w_real(x, y, N):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    yy = y.reshape(l, N*(N+1)//2)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    return CubicSpline(xx, yy)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dc=np.load('data/bayesian/CsF/dc_minimal.npy', allow_pickle = True).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def split_bl(array):\n",
+    "    s = array.shape[0]//2\n",
+    "    return np.hstack([array[:s], array[s:]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using multicomponent code.\n",
+      "Using multicomponent code.\n",
+      "-----------------------------------------------------\n",
+      "  RESAMPLE TIME SERIES\n",
+      "-----------------------------------------------------\n",
+      " Original Nyquist freq  f_Ny =     500.00000 THz\n",
+      " Resampling freq          f* =      20.00000 THz\n",
+      " Sampling time         TSKIP =            25 steps\n",
+      "                             =        25.000 fs\n",
+      " Original  n. of frequencies =         50001\n",
+      " Resampled n. of frequencies =          2001\n",
+      " min(PSD)          (pre-filter&sample) =      0.00208\n",
+      " min(PSD)         (post-filter&sample) =   1856.32621\n",
+      " % of original PSD Power f<f* (pre-filter&sample)  = 99.365 %\n",
+      " fPSD not calculated before resampling\n",
+      " -----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux = st.HeatCurrent(\n",
+    "    [\n",
+    "        # dc['heat_flux'],\n",
+    "        # dc['electric_flux']\n",
+    "        split_bl(dc['heat_flux']),\n",
+    "        split_bl(dc['electric_flux'])\n",
+    "    ],\n",
+    "    DT_FS=1,\n",
+    "    TEMPERATURE=dc['Temperature'],\n",
+    "    VOLUME=dc['Volume'],\n",
+    "    UNITS='metal'\n",
+    "    )\n",
+    "flux_resample=flux.resample(fstar_THz=20)\n",
+    "flux_resample.filter_psd(0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MaxLikeFilter Initialization\n",
+      "Maximum-likelihood estimation with n_parameters = [5 6]\n",
+      "Optimal number of parameters between 5 and 6 chosen by AIC\n",
+      "n_parameters = 5\n",
+      "Spline nodes are equispaced from 0 to the Nyquist frequency.\n",
+      "Optimization terminated successfully.\n",
+      "         Current function value: 75094.365990\n",
+      "         Iterations: 129\n",
+      "         Function evaluations: 4464\n",
+      "         Gradient evaluations: 144\n",
+      "AIC: -75099.3659903142; Steps since last AIC update: 0\n",
+      "n_parameters = 6\n",
+      "Spline nodes are equispaced from 0 to the Nyquist frequency.\n",
+      "Optimization terminated successfully.\n",
+      "         Current function value: 74936.162065\n",
+      "         Iterations: 158\n",
+      "         Function evaluations: 6290\n",
+      "         Gradient evaluations: 170\n",
+      "AIC: -74942.16206470405; Steps since last AIC update: 0\n",
+      "-----------------------------------------------------\n",
+      "  MAXIMUM LIKELIHOOD ESTIMATION\n",
+      "-----------------------------------------------------\n",
+      "  Fixed n_parameters = 6\n",
+      "  S_{00} =      972767.289072 +/- 44343.082278\n",
+      "  S_{01} =      167917.403774 +/- 25834.235931\n",
+      "  S_{11} =      642387.799921 +/- 27425.049491\n",
+      "-----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux_resample.maxlike_estimate(\n",
+    "    lambda x, y: mini_model_w_real(x, y, 2), \n",
+    "    np.arange(5, 7), \n",
+    "    # 5,\n",
+    "    solver = 'BFGS',\n",
+    "    minimize_kwargs = {\n",
+    "        'tol': 1e-8,\n",
+    "        'jac': '3-point',\n",
+    "        'options': {\n",
+    "            'disp': True, \n",
+    "            'gtol': 1e-4, \n",
+    "            'maxiter': 500, \n",
+    "            'eps': 1e-6\n",
+    "            },\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reweight_alpha_vec(alpha, samples):\n",
+    "    truth_mean = np.mean(samples.T[:,:]*np.exp(-alpha[:, None, None]*np.linalg.norm(samples, axis=1)**2), axis=2)\\\n",
+    "    /np.mean(np.exp(-alpha[:, None, None]*np.linalg.norm(samples, axis=1)**2), axis=2)\n",
+    "    truth_var = np.mean(samples.T[:,:]**2*np.exp(-alpha[:, None, None]*np.linalg.norm(samples, axis=1)**2), axis=2)\\\n",
+    "    /np.mean(np.exp(-alpha[:, None, None]*np.linalg.norm(samples, axis=1)**2), axis=2)-truth_mean**2\n",
+    "    return truth_mean, truth_var\n",
+    "\n",
+    "def reweight_logev_alpha_vec(alpha, samples):\n",
+    "    M = samples.shape[1]\n",
+    "    truth_mean = np.log(np.mean(np.exp(-alpha[:, None]*np.linalg.norm(samples, axis=1)**2), axis=1)) + M/2 * np.log(alpha/2/np.pi)\n",
+    "    return truth_mean\n",
+    "\n",
+    "def reweight_alpha(alpha, samples):\n",
+    "    truth_mean = np.mean(samples.T[:,:]*np.exp(-alpha*np.linalg.norm(samples, axis=1)**2), axis=1)\\\n",
+    "    /np.mean(np.exp(-alpha*np.linalg.norm(samples, axis=1)**2), axis=0)\n",
+    "    truth_var = np.mean(samples.T[:,:]**2*np.exp(-alpha*np.linalg.norm(samples, axis=1)**2), axis=1)\\\n",
+    "    /np.mean(np.exp(-alpha*np.linalg.norm(samples, axis=1)**2), axis=0)-truth_mean**2\n",
+    "    return truth_mean, truth_var"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_samples_mc(model, w, omega, omega_fixed, n, cov_w, size=1000):\n",
+    "    sample = w + np.random.multivariate_normal(mean=np.zeros_like(w), cov=cov_w, size=size)\n",
+    "    #sample_S = np.stack([scale_matrix(model, ww, omega, omega_fixed, 2)[:, 1, 0]/scale_matrix(model, ww, omega, omega_fixed, 2)[:, 1, 1] for ww in sample])\n",
+    "    \n",
+    "    return sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sportran.md.maxlike import scale_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampples=generate_samples_mc(flux_resample.maxlike.model,\n",
+    "                             flux_resample.maxlike.parameters_mean,\n",
+    "                             flux_resample.maxlike.omega,\n",
+    "                             flux_resample.maxlike.omega_fixed,\n",
+    "                             flux_resample.N_CURRENTS,\n",
+    "                             flux_resample.maxlike.parameters_cov\n",
+    "                            ) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 18)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampples.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.00000000e-10 1.10009001e-09 2.10018002e-09 ... 9.99799982e-06\n",
+      " 9.99899991e-06 1.00000000e-05]\n",
+      "[9.99121488e-01 9.90377933e-01 9.81710912e-01 ... 1.73263622e-38\n",
+      " 1.71779560e-38 1.70308213e-38]\n",
+      "1.0261923492349234e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "dic_alpha={}\n",
+    "\n",
+    "alpha = np.linspace(1e-10,1e-5,10000)\n",
+    "dic_alpha['lev_s']=reweight_logev_alpha_vec(alpha=alpha,\n",
+    "                                            samples=sampples\n",
+    "                                              )\n",
+    "dic_alpha['alpha_s'] = alpha[np.argmax(dic_alpha['lev_s'])]\n",
+    "print(dic_alpha['alpha_s'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x7fdea2fe9850>"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(alpha, dic_alpha['lev_s'])\n",
+    "plt.axvline(dic_alpha['alpha_s'], color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_p, new_std=reweight_alpha(alpha=dic_alpha['alpha_s'], samples=sampples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1391.19511519,  238.73263035, 1105.96237317, 1196.97269721,\n",
+       "         309.82950296, 1504.6730606 ,  803.52558311, -120.1150033 ,\n",
+       "         712.445139  ,  551.79621607, -168.6288367 ,  206.4561598 ,\n",
+       "         354.3520938 ,  -81.88368176,   55.50049563,  275.65993193,\n",
+       "         -48.13719587,   21.14055301]),\n",
+       " array([1394.82421048,  240.77213818, 1107.6120157 , 1196.74135383,\n",
+       "         309.03265131, 1505.47481376,  803.64908998, -120.54241659,\n",
+       "         712.86097633,  551.80037407, -168.58070723,  206.58579703,\n",
+       "         354.31364229,  -81.88612007,   55.5125449 ,  275.97669283,\n",
+       "         -48.16602338,   21.14914733]))"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_p, flux_resample.maxlike.parameters_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}